

import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


lowerCAmelCase       :				Union[str, Any]         =					{}
for task, _ in pipeline_test_mapping.items():
    lowerCAmelCase       :				Tuple         =					{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
lowerCAmelCase       :				Tuple         =					{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}

def 				A_					(							a					):

      """simple docstring"""



      if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "pt"
      elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "tf"
      elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "flax"
      else:
            return None

def 				A_					(							a   ,				a					):

      """simple docstring"""



      if PIPELINE_TEST_MAPPING[task].get(a   ,				a					) is not None:
            return PIPELINE_TEST_MAPPING[task][framework]

      SCREAMING_SNAKE_CASE_			:					str          =       pipeline_test_mapping[task]['test']
      SCREAMING_SNAKE_CASE_			:					List[str]          =       None

      if framework == "pt":
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       getattr(a   ,				'model_mapping'   ,				a					)
      elif framework == "tf":
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       getattr(a   ,				'tf_model_mapping'   ,				a					)

      if mapping is not None:
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       dict(mapping.items()					)

      # cache the results
      SCREAMING_SNAKE_CASE_			:					List[str]          =       mapping
      return mapping

def 				A_					(							a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Dict          =       get_framework(a					)
      if framework is None:
            return None
      SCREAMING_SNAKE_CASE_			:					int          =       get_mapping_for_task(a   ,				a					)
      if mapping is None:
            return None

      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       list({model_class.config_class for model_class in test_class.all_model_classes}					)
      if len(a					) != 1:
            raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'					)

      # This could be a list/tuple of model classes, but it's rare.
      SCREAMING_SNAKE_CASE_			:					Tuple          =       mapping.get(config_classes[0]   ,				a					)
      if isinstance(a   ,				(tuple, list)					):
            SCREAMING_SNAKE_CASE_			:					Tuple          =       sorted(a   ,				key=lambda a					: x.__name__					)

      return model_class

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       [(task, get_model_for_pipeline_test(a   ,				a					)) for task in pipeline_test_mapping]
      SCREAMING_SNAKE_CASE_			:					Tuple          =       sorted([(task, model) for task, model in mapping if model is not None]   ,				key=lambda a					: x[0]					)

      return dict(a					)

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Tuple          =       get_framework(a					)
      if framework == "pt":
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       'torch'
      SCREAMING_SNAKE_CASE_			:					int          =       '{}'

      SCREAMING_SNAKE_CASE_			:					Dict          =       get_pipeline_model_mapping(a					)
      if len(a					) == 0:
            return ""

      SCREAMING_SNAKE_CASE_			:					str          =       []
      for task, model_classes in mapping.items():
            if isinstance(a   ,				(tuple, list)					):
                  # A list/tuple of model classes
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       '(' + ', '.join([x.__name__ for x in model_classes]					) + ')'
            else:
                  # A single model class
                  SCREAMING_SNAKE_CASE_			:					str          =       model_classes.__name__
            texts.append(f"\"{task}\": {value}"					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       '{' + ', '.join(a					) + '}'
      SCREAMING_SNAKE_CASE_			:					Tuple          =       f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

      return text

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       {'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
      if not issubclass(a   ,				unittest.TestCase					):
            return False
      return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Tuple          =       [x for x in get_test_classes(a					) if is_valid_test_class(a					)]

      SCREAMING_SNAKE_CASE_			:					int          =       None
      for test_class in test_classes:
            # If a test class has defined `pipeline_model_mapping`, let's take it
            if getattr(a   ,				'pipeline_model_mapping'   ,				a					) is not None:
                  SCREAMING_SNAKE_CASE_			:					Dict          =       test_class
                  break
    # Take the test class with the shortest name (just a heuristic)
      if target_test_class is None and len(a					) > 0:
            SCREAMING_SNAKE_CASE_			:					str          =       sorted(a   ,				key=lambda a					: (len(x.__name__					), x.__name__)					)[0]

      return target_test_class

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Tuple          =       start_idx
      for idx, line in enumerate(lines[start_idx:]					):
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       len(a					) - len(line.lstrip()					)
            if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
                  SCREAMING_SNAKE_CASE_			:					Any          =       start_idx + idx
            elif idx > 0 and indent <= indent_level:
                  # Outside the definition block of `pipeline_model_mapping`
                  break

      return end_idx

def 				A_					(							a   ,				a=False					):

      """simple docstring"""



      if getattr(a   ,				'pipeline_model_mapping'   ,				a					) is not None:
            if not overwrite:
                  return "", -1

      SCREAMING_SNAKE_CASE_			:					str          =       get_pipeline_model_mapping_string(a					)
      if len(a					) == 0:
            return "", -1
      SCREAMING_SNAKE_CASE_			:					Tuple          =       line_to_add + '\n'

      # The code defined the class `test_class`
      SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Dict          =       inspect.getsourcelines(a					)
      # `inspect` gives the code for an object, including decorator(s) if any.
      # We (only) need the exact line of the class definition.
      for idx, line in enumerate(a					):
            if line.lstrip().startswith('class '					):
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       class_lines[idx:]
                  class_start_line_no += idx
                  break
      SCREAMING_SNAKE_CASE_			:					int          =       class_start_line_no + len(a					) - 1

      # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
      # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
      # block that appears in a `test_class`.
      SCREAMING_SNAKE_CASE_			:					List[str]          =       None
      # The indent level of the line at `class_lines[start_idx]` (if defined)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       0
      # To record if `pipeline_model_mapping` is found in `test_class`.
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       None
      for idx, line in enumerate(a					):
            if line.strip().startswith('all_model_classes = '					):
                  SCREAMING_SNAKE_CASE_			:					Any          =       len(a					) - len(line.lstrip()					)
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       idx
            elif line.strip().startswith('all_generative_model_classes = '					):
                  SCREAMING_SNAKE_CASE_			:					Any          =       len(a					) - len(line.lstrip()					)
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       idx
            elif line.strip().startswith('pipeline_model_mapping = '					):
                  SCREAMING_SNAKE_CASE_			:					Dict          =       len(a					) - len(line.lstrip()					)
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       idx
                  SCREAMING_SNAKE_CASE_			:					Any          =       line
                  break

      if start_idx is None:
            return "", -1
      # Find the ending index (inclusive) of the above found block.
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       find_block_ending(a   ,				a   ,				a					)

      # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
      # `is_timm_available()` instead of `is_torch_available()`.
      # Keep leading and trailing whitespaces
      SCREAMING_SNAKE_CASE_			:					List[str]          =       re.compile(R'\s(is_\S+?_available\(\))\s'					)
      for line in class_lines[start_idx : end_idx + 1]:
            SCREAMING_SNAKE_CASE_			:					Any          =       r.search(a					)
            if backend_condition is not None:
                  # replace the leading and trailing whitespaces to the space character " ".
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       ' ' + backend_condition[0][1:-1] + ' '
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       r.sub(a   ,				a					)
                  break

      if def_line is None:
            # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
            # `all_model_classes` or `all_generative_model_classes`.
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       end_idx
      else:
            # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
            SCREAMING_SNAKE_CASE_			:					List[str]          =       start_idx - 1
            # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
            for idx in range(a   ,				end_idx + 1					):
                  # These lines are going to be removed before writing to the test file.
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
      SCREAMING_SNAKE_CASE_			:					Any          =       [x.__name__ for x in test_class.__bases__]
      if "PipelineTesterMixin" not in parent_classes:
            # Put `PipelineTesterMixin` just before `unittest.TestCase`
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       [x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
            if "TestCase" in parent_classes:
                  # Here we **assume** the original string is always with `unittest.TestCase`.
                  _parent_classes.append('unittest.TestCase'					)
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       ', '.join(_parent_classes					)
            for idx, line in enumerate(a					):
                  # Find the ending of the declaration of `test_class`
                  if line.strip().endswith('):'					):
                        # mark the lines of the declaration of `test_class` to be removed
                        for _idx in range(idx + 1					):
                              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       None  # noqa
                        break
        # Add the new, one-line, class declaration for `test_class`
            SCREAMING_SNAKE_CASE_			:					Tuple          =       f"class {test_class.__name__}({parent_classes}):\n"

      # Add indentation
      SCREAMING_SNAKE_CASE_			:					str          =       ' ' * indent_level + line_to_add
      # Insert `pipeline_model_mapping` to `class_lines`.
      # (The line at `target_idx` should be kept by definition!)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
      # Remove the lines that are marked to be removed
      SCREAMING_SNAKE_CASE_			:					str          =       [x for x in class_lines if x is not None]

      # Move from test class to module (in order to write to the test file)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       inspect.getsourcelines(inspect.getmodule(a					)					)[0]
      # Be careful with the 1-off between line numbers and array indices
      SCREAMING_SNAKE_CASE_			:					List[str]          =       module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
      SCREAMING_SNAKE_CASE_			:					Tuple          =       ''.join(a					)

      SCREAMING_SNAKE_CASE_			:					Any          =       inspect.getsourcefile(a					)
      with open(a   ,				'w'   ,				encoding='UTF-8'   ,				newline='\n'					) as fp:
            fp.write(a					)

      return line_to_add

def 				A_					(							a   ,				a=False					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Dict          =       find_test_class(a					)
      if test_class:
            add_pipeline_model_mapping(a   ,				overwrite=a					)


if __name__ == "__main__":
    lowerCAmelCase       :				str         =					argparse.ArgumentParser()
    parser.add_argument(
        '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
    )
    parser.add_argument(
        '--all',
        action='store_true',
        help='If to check and modify all test files.',
    )
    parser.add_argument(
        '--overwrite',
        action='store_true',
        help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
    )
    lowerCAmelCase       :				int         =					parser.parse_args()

    if not args.all and not args.test_file:
        raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
    elif args.all and args.test_file:
        raise ValueError('Only one of `--test_file` and `--all` could be specified.')

    lowerCAmelCase       :				Union[str, Any]         =					[]
    if args.test_file:
        lowerCAmelCase       :				Union[str, Any]         =					[args.test_file]
    else:
        lowerCAmelCase       :				Optional[int]         =					os.path.join('tests', 'models', '**', 'test_modeling_*.py')
        for test_file in glob.glob(pattern):
            # `Flax` is not concerned at this moment
            if not test_file.startswith('test_modeling_flax_'):
                test_files.append(test_file)

    for test_file in test_files:
        if test_file in TEST_FILE_TO_IGNORE:
            print(F'[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.')
            continue
        add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)









import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       :				str         =					logging.get_logger(__name__)

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					str          =       tf.image.resize(a   ,				(3_4_6, 3_4_6)					)
      SCREAMING_SNAKE_CASE_			:					Any          =       tf.image.crop_to_bounding_box(a   ,				(3_4_6 - 2_8_9) // 2   ,				(3_4_6 - 2_8_9) // 2   ,				2_8_9   ,				2_8_9					)
      return image

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       EfficientNetConfig.from_pretrained('google/efficientnet-b7'					)
      SCREAMING_SNAKE_CASE_			:					int          =       2_8_9
      SCREAMING_SNAKE_CASE_			:					int          =       6_4_0
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       {'0': 'LABEL_0', '1': 'LABEL_1'}
      SCREAMING_SNAKE_CASE_			:					Tuple          =       {'LABEL_0': 0, 'LABEL_1': 1}
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       []

      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       BertConfig()
      SCREAMING_SNAKE_CASE_			:					Dict          =       AlignConfig.from_text_vision_configs(
          text_config=a   ,				vision_config=a   ,				projection_dim=6_4_0					)
      return config

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Dict          =       'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       Image.open(requests.get(a   ,				stream=a					).raw					)
      return im

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       EfficientNetImageProcessor(
          do_center_crop=a   ,				rescale_factor=1 / 1_27.5   ,				rescale_offset=a   ,				do_normalize=a   ,				include_top=a   ,				resample=Image.BILINEAR   ,				)
      SCREAMING_SNAKE_CASE_			:					List[str]          =       BertTokenizer.from_pretrained('bert-base-uncased'					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       6_4
      SCREAMING_SNAKE_CASE_			:					Tuple          =       AlignProcessor(image_processor=a   ,				tokenizer=a					)
      return processor

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       [v.split('_'					)[0].split('block'					)[1] for v in original_param_names if v.startswith('block'					)]
      SCREAMING_SNAKE_CASE_			:					Any          =       list(set(a					)					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       sorted(a					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					int          =       {b: str(a					) for b, i in zip(a   ,				range(a					)					)}

      SCREAMING_SNAKE_CASE_			:					str          =       []
      rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')					)
      rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')					)
      rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')					)
      rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')					)
      rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')					)

      for b in block_names:
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       block_name_mapping[b]
            rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")					)
            rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")					)
            rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")					)
            rename_keys.append(
                (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")					)
            rename_keys.append(
                (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")					)
            rename_keys.append(
                (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")					)
            rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")					)
            rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")					)
            rename_keys.append(
                (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")					)
            rename_keys.append(
                (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")					)

            rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")					)
            rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")					)
            rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")					)
            rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")					)
            rename_keys.append(
                (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")					)
            rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")					)
            rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")					)
            rename_keys.append(
                (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")					)
            rename_keys.append(
                (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")					)

      SCREAMING_SNAKE_CASE_			:					List[str]          =       {}
      for item in rename_keys:
            if item[0] in original_param_names:
                  SCREAMING_SNAKE_CASE_			:					Any          =       'vision_model.' + item[1]

    # BERT text encoder
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       []
      SCREAMING_SNAKE_CASE_			:					Tuple          =       'tf_bert_model/bert'
      SCREAMING_SNAKE_CASE_			:					Any          =       'text_model'
      for i in range(1_2					):
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.query.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.query.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.key.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.key.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.value.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.value.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.output.dense.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
                    f"{new}.encoder.layer.{i}.attention.output.dense.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
                    f"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
                    f"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
                    f"{new}.encoder.layer.{i}.intermediate.dense.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
                    f"{new}.encoder.layer.{i}.intermediate.dense.bias",
                )					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/dense/kernel:0", f"{new}.encoder.layer.{i}.output.dense.weight")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/dense/bias:0", f"{new}.encoder.layer.{i}.output.dense.bias")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", f"{new}.encoder.layer.{i}.output.LayerNorm.weight")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", f"{new}.encoder.layer.{i}.output.LayerNorm.bias")					)

      rename_keys.append((f"{old}/embeddings/word_embeddings/weight:0", f"{new}.embeddings.word_embeddings.weight")					)
      rename_keys.append(
          (f"{old}/embeddings/position_embeddings/embeddings:0", f"{new}.embeddings.position_embeddings.weight")					)
      rename_keys.append(
          (f"{old}/embeddings/token_type_embeddings/embeddings:0", f"{new}.embeddings.token_type_embeddings.weight")					)
      rename_keys.append((f"{old}/embeddings/LayerNorm/gamma:0", f"{new}.embeddings.LayerNorm.weight")					)
      rename_keys.append((f"{old}/embeddings/LayerNorm/beta:0", f"{new}.embeddings.LayerNorm.bias")					)

      rename_keys.append((f"{old}/pooler/dense/kernel:0", f"{new}.pooler.dense.weight")					)
      rename_keys.append((f"{old}/pooler/dense/bias:0", f"{new}.pooler.dense.bias")					)
      rename_keys.append(('dense/kernel:0', 'text_projection.weight')					)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')					)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')					)
      rename_keys.append(('temperature:0', 'temperature')					)

      for item in rename_keys:
            if item[0] in original_param_names:
                  SCREAMING_SNAKE_CASE_			:					int          =       item[1]
      return key_mapping

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      list(hf_params.keys()					)

      for key, value in tf_params.items():
            if key not in key_mapping:
                  continue

            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					str          =       torch.from_numpy(a					).permute(3   ,				2   ,				0   ,				1					)
            elif "embeddings" in key:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.from_numpy(a					)
            elif "depthwise_kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					Dict          =       torch.from_numpy(a					).permute(2   ,				3   ,				0   ,				1					)
            elif "kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					Any          =       torch.from_numpy(np.transpose(a					)					)
            elif "temperature" in key:
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       value
            elif "bn/gamma" or "bn/beta" in key:
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.from_numpy(np.transpose(a					)					).squeeze()
            else:
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.from_numpy(a					)

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(a					)

@torch.no_grad()
def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       6_4
      SCREAMING_SNAKE_CASE_			:					List[str]          =       Tokenizer(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       align.Align('efficientnet-b7'   ,				'bert-base'   ,				6_4_0   ,				a   ,				tok.get_vocab_size()					)
      original_model.compile()
      original_model.load_weights(a					)

      SCREAMING_SNAKE_CASE_			:					Dict          =       original_model.trainable_variables
      SCREAMING_SNAKE_CASE_			:					int          =       original_model.non_trainable_variables
      SCREAMING_SNAKE_CASE_			:					str          =       {param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       param.numpy()
      SCREAMING_SNAKE_CASE_			:					Any          =       list(tf_params.keys()					)

      # Load HuggingFace model
      SCREAMING_SNAKE_CASE_			:					Any          =       get_align_config()
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       AlignModel(a					).eval()
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'					)
      SCREAMING_SNAKE_CASE_			:					str          =       rename_keys(a					)
      replace_params(a   ,				a   ,				a					)

      # Initialize processor
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       get_processor()
      SCREAMING_SNAKE_CASE_			:					List[str]          =       processor(
          images=prepare_img()   ,				text='A picture of a cat'   ,				padding='max_length'   ,				max_length=6_4   ,				return_tensors='pt'					)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       hf_model(**a					)

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       outputs.image_embeds.detach().numpy()
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       outputs.text_embeds.detach().numpy()

      # Original model inference
      SCREAMING_SNAKE_CASE_			:					Dict          =       False
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       EfficientNetImageProcessor(
          do_center_crop=a   ,				do_rescale=a   ,				do_normalize=a   ,				include_top=a   ,				resample=Image.BILINEAR   ,				)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       tf_image_processor(images=prepare_img()   ,				return_tensors='tf'   ,				data_format='channels_last'					)['pixel_values']
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       tok(tf.constant(['A picture of a cat']					)					)

      SCREAMING_SNAKE_CASE_			:					List[str]          =       original_model.image_encoder(a   ,				training=a					)
      SCREAMING_SNAKE_CASE_			:					Any          =       original_model.text_encoder(a   ,				training=a					)

      SCREAMING_SNAKE_CASE_			:					str          =       tf.nn.la_normalize(a   ,				axis=-1					)
      SCREAMING_SNAKE_CASE_			:					int          =       tf.nn.la_normalize(a   ,				axis=-1					)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(a   ,				a   ,				atol=1e-3					):
            raise ValueError('The predicted image features are not the same.'					)
      if not np.allclose(a   ,				a   ,				atol=1e-3					):
            raise ValueError('The predicted text features are not the same.'					)
      print('Model outputs match!'					)

      if save_model:
            # Create folder to save model
            if not os.path.isdir(a					):
                  os.mkdir(a					)
            # Save converted model and image processor
            hf_model.save_pretrained(a					)
            processor.save_pretrained(a					)

      if push_to_hub:
            # Push model and image processor to hub
            print('Pushing converted ALIGN to the hub...'					)
            processor.push_to_hub('align-base'					)
            hf_model.push_to_hub('align-base'					)


if __name__ == "__main__":
    lowerCAmelCase       :				str         =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint_path',
        default='./weights/model-weights',
        type=str,
        help='Path to the pretrained TF ALIGN checkpoint.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='hf_model',
        type=str,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--save_model', action='store_true', help='Save model to local')
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

    lowerCAmelCase       :				str         =					parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






