* Updated from upstream TF

* Manually copied changes from http://cl/449226814 and http://cl/449192972

* fix missing include (erroneously removed in previous commit).

* fix the hexagon build.

* First pass working with float input/output

* Numpy buffer input works. Got a sine graph for hello world output.

Copyright notice

* Remove static references

* Output tensors work

* Output tensors work

* bazel test works

bazel test tensorflow/lite/micro/tools:interpreter_test
--test_output=all

* Successfully built extension with Python setuptools

cd tensorflow/lite/micro/tools/interpreter_pypi
python -m build
pip install dist/example_tflm_interpreter_psho-0.0.3-cp39-cp39-linux_x86_64.whl --force-reinstall
python tests/test-interpreter.py

TestPyPi:
pip uninstall example-tflm-interpreter-psho
python -m twine upload --repository testpypi dist/example-tflm-interpreter-psho-0.0.3.tar.gz
pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple example-tflm-interpreter-psho==0.0.3
python tests/test-interpreter.py

* Refactored some

* Cleaned for production and tested with conv model

* Fixed formatting

* Fixed numpy header issue for Docker. Need to fix double registration issue that's somehow only present in Docker.

* Test fix with changes in ci.yml

* Added sudo

* Added nomsan and noasan tags to python test

* Added -layering-check, used py_library instead of py_binary to generate conv model, added micro namespace

* Removed extra debug code

* Added new tests

* Changed reinterpret_steal to cast

* Addressed PR comments

* size log refactor (#1156)
Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Addressed PR and review comments

* adding documentation to log_binary* workflows. (#1161)

* Automated binary size log update (#1160)

* Create an allocator to manage persistent arena (#1174)

* Create an allocator to manage persistent arena

This PR corresponds to internal cl/452380512.

Create an allocator to manage persistent arena.
This is another step towards the feature of enabling the client to
have two separate memory arenas.

BUG=https://b/226971240

* Manually patch due to Makefile difference

* Fix virtual environment when building Vela for Arm Ethos-U (#1177)
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* Removed print statement
Co-authored-by: Njwithers <jpwithers@gmail.com>
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Co-authored-by: Ndeqiangc <86809673+deqiangc@users.noreply.github.com>
Co-authored-by: NMåns Nilsson <mans.nilsson@arm.com>

* Remove unused scratch buffer for integer mode LSTM

* Revert changes to lstm_eval
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* Added Support for INT8 pad

BUG= Added support for INT8 Pad
 Fixed padding issues in conv for larger tiles

* Reverted submits to main

* Revert "Added Support for INT8 pad"

This reverts commit fdc6e1d624609041b8b743563613339948ff70da.

* Added Support for INT8 Pad

BUG= Added support for INT8 Pad. Fixed padding issue in conv.

* TFLITE_DCHECK xiPadGetMemReqd_Context() status
Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>

Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Create an allocator to manage persistent arena

This PR corresponds to internal cl/452380512.

Create an allocator to manage persistent arena.
This is another step towards the feature of enabling the client to
have two separate memory arenas.

BUG=https://b/226971240

* Manually patch due to Makefile difference

Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Refactor to share code between reference and optimized kernels.

The goal is to reduce code duplication, improve readability of the
optimized kernels and reduce the maintenance overhead.

* Fix bazel build (and hopefully others as well).
Co-authored-by: NPauline Sho <psho@google.com>

Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>
Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Fix create tree script to support third party outside downloads folder.

Change-Id: I27c3004d6f7295456c659b3bb98f94a55d95eb1b

* Fix formatting
Co-authored-by: NPauline Sho <psho@google.com>
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* explicitly convert to float and change variable names

* Change variable name to accurately reflect type

- Issue fixed in 5-17-22 xi_tflmlib_vision_p6

BUG=b/228102789

This corresponds to cl/9257066. This allows some variant
ADD kernel optimization.

BUG=https://b/231368244Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* BUG= Added Support for ReduceMax on Vision P6

* BUG= Updated for namespace use

* BUG= Fixed build issue for non VP6 cores

* updated as per the review comments
Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>

* Quantization specific registration for CMSIS-NN

Adds three specific registrations for softmax kernel:
- Pure int8.
- Pure int16.
- And one with int8 input and int16 output.

In order to avoid duplicating code CalculateSoftmaxParams is made
public.

Change-Id: I51de85e85f3bfb7a2d936593bd6512f263e6e5e4

* Add helper function InitializeLutForInt16

* Do not inline helper function
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Co-authored-by: NAdvait Jain <advaitjain@users.noreply.github.com>

BUG=b/228102789
Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>
Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Add additional visibility to micro_test

BUG=https://b/232264277

* Fix typo
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BUG=b/228102789
Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

* Copy squared_diffence* from TFLite verbatim

* Remove TFLite specific contents and flatten namespaces

* Complete porting squared difference to micro

* Updated based on comments
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* Quantization specific registration for CMSIS-NN fully connected int16

Adds int16 support, int16 specific registration and unit test (ported
from TFL) for CMSIS-NN fully connected.

Change-Id: I03fed7eef1880c0796785791225e618322c51642

* Add int16 support to fully_connected reference kernel

* CMSIS-NN; Add PopulateCommonParams in FC

* Fix formatting
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* Quantization specific registration for CMSIS-NN

Adds int8 and int16x8 specific registrations for depthwise conv
kernel.

Change-Id: I649c65f642777b310318cbb535bec69e41d20314

* CMSIS-NN: Initialize common DW conv parameters in macro

* Switch from macro to function

* Fix automatic merge issue
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* Support for Fully Connected on Vision P6

BUG= Added support for Fully Connected operation on vision P6

* Update xtensa.inc

BUG= Added Missing file in makefile

* Update fully_connected_vision.cc

BUG= fixed compilation issue

* Update fully_connected_vision.cc

BUG=Updated review comments by RJ

* Update fully_connected_vision.cc

updated review comments

* Update fully_connected.cc

Updated as per review comments
Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>

* Handle the relative path in generate_cc_arrays.py

This corresponds to internal cl/447815949.
This changes handles the relative input path to generate_cc_arrays.py

BUG=https://b/199442906

* Remove obsolete print
Co-authored-by: NPauline Sho <psho@google.com>

* Cadence: Pytorch(mobilenet_v2) to tflite quantized

Describved two methos to convert from pytorch to tflite
1. Using TinyNN tool
2. Using pytorch to onnx to tf(saved_model) to
tflite(float32)/tflite(int8 quantized)

* Update pytorch_to_tflite_test.cc

* Update pytorch_images_dog_jpg.cc

* Remove unnecessary space

* Change HIFI4 and HIFI4_INTERNAL #ifndef locations and apply clang-format
Co-authored-by: NTing Yan <94130036+tingyan19@users.noreply.github.com>

Change-Id: I5d52a728dbb22a3bf838834948e4f0cf8fa6ba41
Co-authored-by: Nmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

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* Add Uint8 Dequantize

While TFLM still doesn't allow uint8 quantization
(https://github.com/tensorflow/tflite-micro/issues/216), there are
use-cases where having uint8 for quantize/dequantize/requantize can
enable legacy models. Restore support for uint8 dequantize.

Tested:
dequantize_test, running TF1.x SSD models on Coral Dev Board Micro

* Add Uint8 Dequantize

While TFLM still doesn't allow uint8 quantization
(https://github.com/tensorflow/tflite-micro/issues/216), there are
use-cases where having uint8 for quantize/dequantize/requantize can
enable legacy models. Restore support for uint8 dequantize.

Tested:
dequantize_test, running TF1.x SSD models on Coral Dev Board Micro

BUG=b/213518048
Co-authored-by: NPauline Sho <psho@google.com>

* Add support quantize(uint8<->int8)

* Add support quantize(uint8<->int8)

BUG=b/213518048

* Sync from upstream TF. (#1095)

* Automated size log update (#1100)
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* Automated size log update (#1103)

* Improved quantize tests with more meaningful parameters.
Tested with converter flags
```
  converter.inference_input_type = tf.uint8
  converter.inference_output_type = tf.int8
```
and
```
  converter.inference_input_type = tf.int8
  converter.inference_output_type = tf.uint8
```
and verified that the correct quantization was invoked.

* Remove extra copy of test and add comments

* Replaced logging with MicroPrintf

* Fixed MicroPrintf typo

* Exclude XTENSA in unit tests
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