code: 
  
  
  # Copyright 2021 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import json
  import os
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import yaml
  
  from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
  from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION
  
  
  __a   				=      os.path.expanduser(
      os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
  )
  __a   				=      os.path.join(hf_cache_home, '''accelerate''')
  __a   				=      os.path.join(cache_dir, '''default_config.yaml''')
  __a   				=      os.path.join(cache_dir, '''default_config.yaml''')
  
  # For backward compatibility: the default config is the json one if it's the only existing file.
  if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
    __a   				=      default_yaml_config_file
  else:
    __a   				=      default_json_config_file
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if config_file is not None:
              if not os.path.isfile(_UpperCamelCase     ):
                    raise FileNotFoundError(
                        f"""The passed configuration file `{config_file}` does not exist. """
                        '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                        '''created through `accelerate config` and run `accelerate launch` '''
                        '''without the `--config_file` argument.'''     )
        else:
              lowercase			:   List[Any]					   =		default_config_file
        with open(_UpperCamelCase,    '''r''',    encoding='''utf-8'''     ) as f:
              if config_file.endswith('''.json'''     ):
                    if (
                        json.load(_UpperCamelCase     ).get('''compute_environment''',    ComputeEnvironment.LOCAL_MACHINE     )
                        == ComputeEnvironment.LOCAL_MACHINE
                    ):
                          lowercase			:   int					   =		ClusterConfig
                    else:
                          lowercase			:   Dict					   =		SageMakerConfig
                    return config_class.from_json_file(json_file=_UpperCamelCase     )
              else:
                    if (
                        yaml.safe_load(_UpperCamelCase     ).get('''compute_environment''',    ComputeEnvironment.LOCAL_MACHINE     )
                        == ComputeEnvironment.LOCAL_MACHINE
                    ):
                          lowercase			:   int					   =		ClusterConfig
                    else:
                          lowercase			:   Optional[Any]					   =		SageMakerConfig
                    return config_class.from_yaml_file(yaml_file=_UpperCamelCase     )
  
  
  
  
  
  
  
  @dataclass
  class 				__SCREAMING_SNAKE_CASE   :
        A				:							ComputeEnvironment
        A				:							Union[DistributedType, SageMakerDistributedType]
        A				:							str
        A				:							bool
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		self.__dict__
              # For serialization, it's best to convert Enums to strings (or their underlying value type).
              for key, value in result.items():
                    if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                          lowercase			:   List[Any]					   =		value.value
                    if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) and not bool(SCREAMING_SNAKE_CASE__		):
                          lowercase			:   List[Any]					   =		None
              lowercase			:   Dict					   =		{k: v for k, v in result.items() if v is not None}
              return result
  
  
        @classmethod
        def        __lowerCamelCase			(							cls							,						SCREAMING_SNAKE_CASE__=None		):
              lowercase			:   Optional[int]					   =		default_json_config_file if json_file is None else json_file
              with open(SCREAMING_SNAKE_CASE__							,						'''r'''							,						encoding='''utf-8'''		) as f:
                    lowercase			:   int					   =		json.load(SCREAMING_SNAKE_CASE__		)
              if "compute_environment" not in config_dict:
                    lowercase			:   Union[str, Any]					   =		ComputeEnvironment.LOCAL_MACHINE
              if "mixed_precision" not in config_dict:
                    lowercase			:   int					   =		'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
              if "fp16" in config_dict:  # Convert the config to the new format.
                    del config_dict["fp16"]
              if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                    lowercase			:   Tuple					   =		config_dict.pop('''dynamo_backend'''		)
                    lowercase			:   Tuple					   =		{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
              if "use_cpu" not in config_dict:
                    lowercase			:   Optional[Any]					   =		False
              return cls(**SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              with open(SCREAMING_SNAKE_CASE__							,						'''w'''							,						encoding='''utf-8'''		) as f:
                    lowercase			:   Optional[int]					   =		json.dumps(self.to_dict()							,						indent=2							,						sort_keys=SCREAMING_SNAKE_CASE__		) + '''\n'''
                    f.write(SCREAMING_SNAKE_CASE__		)
  
  
        @classmethod
        def        __lowerCamelCase			(							cls							,						SCREAMING_SNAKE_CASE__=None		):
              lowercase			:   Union[str, Any]					   =		default_yaml_config_file if yaml_file is None else yaml_file
              with open(SCREAMING_SNAKE_CASE__							,						'''r'''							,						encoding='''utf-8'''		) as f:
                    lowercase			:   List[str]					   =		yaml.safe_load(SCREAMING_SNAKE_CASE__		)
              if "compute_environment" not in config_dict:
                    lowercase			:   int					   =		ComputeEnvironment.LOCAL_MACHINE
  
              if "mixed_precision" not in config_dict:
                    lowercase			:   Optional[int]					   =		'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
              if isinstance(config_dict['''mixed_precision''']							,						SCREAMING_SNAKE_CASE__		) and not config_dict["mixed_precision"]:
                    lowercase			:   List[str]					   =		'''no'''
              if "fp16" in config_dict:  # Convert the config to the new format.
                    del config_dict["fp16"]
              if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                    lowercase			:   Optional[Any]					   =		config_dict.pop('''dynamo_backend'''		)
                    lowercase			:   int					   =		{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
              if "use_cpu" not in config_dict:
                    lowercase			:   int					   =		False
              return cls(**SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              with open(SCREAMING_SNAKE_CASE__							,						'''w'''							,						encoding='''utf-8'''		) as f:
                    yaml.safe_dump(self.to_dict()							,						SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							self		):
              if isinstance(self.compute_environment							,						SCREAMING_SNAKE_CASE__		):
                    lowercase			:   Tuple					   =		ComputeEnvironment(self.compute_environment		)
              if isinstance(self.distributed_type							,						SCREAMING_SNAKE_CASE__		):
                    if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                          lowercase			:   Any					   =		SageMakerDistributedType(self.distributed_type		)
                    else:
                          lowercase			:   Union[str, Any]					   =		DistributedType(self.distributed_type		)
              if self.dynamo_config is None:
                    lowercase			:   str					   =		{}
  
  
  
  
  
  
  
  @dataclass
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							int
        A				:							int    							=     0
        A				:							int    							=     1
        A				:							Optional[str]    							=     None
        A				:							Optional[str]    							=     None
        A				:							Optional[int]    							=     None
        A				:							Optional[str]    							=     "static"
        A				:							Optional[bool]    							=     False
        A				:							str    							=     "main"
  
        # args for deepspeed_plugin
        A				:							dict    							=     None
        # args for fsdp
        A				:							dict    							=     None
        # args for megatron_lm
        A				:							dict    							=     None
        # args for ipex
        A				:							dict    							=     None
        # args for TPU
        A				:							bool    							=     False
  
        # args for TPU pods
        A				:							str    							=     None
        A				:							str    							=     None
        A				:							bool    							=     False
        A				:							bool    							=     False
        A				:							str    							=     None
        A				:							List[str]    							=     None
        A				:							List[str]    							=     None
        A				:							List[str]    							=     None
  
        # args for dynamo
        A				:							dict    							=     None
  
  
        def        __lowerCamelCase			(							self		):
              if self.deepspeed_config is None:
                    lowercase			:   int					   =		{}
              if self.fsdp_config is None:
                    lowercase			:   Optional[int]					   =		{}
              if self.megatron_lm_config is None:
                    lowercase			:   Tuple					   =		{}
              if self.ipex_config is None:
                    lowercase			:   Optional[Any]					   =		{}
              return super().__post_init__()
  
  
  
  
  
  
  
  @dataclass
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							str
        A				:							str
        A				:							Optional[str]    							=     None
        A				:							Optional[str]    							=     None
        A				:							str    							=     "us-east-1"
        A				:							int    							=     1
        A				:							str    							=     "all"
        A				:							str    							=     F'accelerate-sagemaker-{num_machines}'
        A				:							str    							=     SAGEMAKER_PYTORCH_VERSION
        A				:							str    							=     SAGEMAKER_TRANSFORMERS_VERSION
        A				:							str    							=     SAGEMAKER_PYTHON_VERSION
        A				:							str    							=     None
        A				:							str    							=     None
        A				:							dict    							=     None
        A				:							dict    							=     None
  
  
style_context: 
  
  
  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()
  __a   				=      logging.get_logger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		tf.image.resize(_UpperCamelCase,    (346, 346)     )
        lowercase			:   Any					   =		tf.image.crop_to_bounding_box(_UpperCamelCase,    (346 - 289) // 2,    (346 - 289) // 2,    289,    289     )
        return image
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''     )
        lowercase			:   Optional[int]					   =		289
        lowercase			:   Optional[Any]					   =		640
        lowercase			:   Any					   =		{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
        lowercase			:   Optional[Any]					   =		{'''LABEL_0''': 0, '''LABEL_1''': 1}
        lowercase			:   Union[str, Any]					   =		[]
  
        lowercase			:   Union[str, Any]					   =		BertConfig()
        lowercase			:   Any					   =		AlignConfig.from_text_vision_configs(
            text_config=_UpperCamelCase,    vision_config=_UpperCamelCase,    projection_dim=640     )
        return config
  
  
  
  
  def 			__lowercase	(						)    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
        lowercase			:   int					   =		Image.open(requests.get(_UpperCamelCase,    stream=_UpperCamelCase     ).raw     )
        return im
  
  
  
  
  def 			__lowercase	(						)    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    rescale_factor=1 / 1_2_7.5,    rescale_offset=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   Any					   =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )
        lowercase			:   int					   =		64
        lowercase			:   List[Any]					   =		AlignProcessor(image_processor=_UpperCamelCase,    tokenizer=_UpperCamelCase     )
        return processor
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		[v.split('''_'''     )[0].split('''block'''     )[1] for v in original_param_names if v.startswith('''block'''     )]
        lowercase			:   str					   =		list(set(_UpperCamelCase     )     )
        lowercase			:   List[str]					   =		sorted(_UpperCamelCase     )
        lowercase			:   str					   =		len(_UpperCamelCase     )
        lowercase			:   Dict					   =		{b: str(_UpperCamelCase     ) for b, i in zip(_UpperCamelCase,    range(_UpperCamelCase     )     )}
  
        lowercase			:   int					   =		[]
        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:
              lowercase			:   Dict					   =		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""")     )
  
        lowercase			:   Union[str, Any]					   =		{}
        for item in rename_keys:
              if item[0] in original_param_names:
                    lowercase			:   Dict					   =		'''vision_model.''' + item[1]
  
      # BERT text encoder
        lowercase			:   str					   =		[]
        lowercase			:   Optional[Any]					   =		'''tf_bert_model/bert'''
        lowercase			:   str					   =		'''text_model'''
        for i in range(12     ):
              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:
                    lowercase			:   Tuple					   =		item[1]
        return key_mapping
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        list(hf_params.keys()     )
  
        for key, value in tf_params.items():
              if key not in key_mapping:
                    continue
  
              lowercase			:   List[str]					   =		key_mapping[key]
              if "_conv" in key and "kernel" in key:
                    lowercase			:   List[Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(3,    2,    0,    1     )
              elif "embeddings" in key:
                    lowercase			:   int					   =		torch.from_numpy(_UpperCamelCase     )
              elif "depthwise_kernel" in key:
                    lowercase			:   Union[str, Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(2,    3,    0,    1     )
              elif "kernel" in key:
                    lowercase			:   int					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     )
              elif "temperature" in key:
                    lowercase			:   Optional[int]					   =		value
              elif "bn/gamma" or "bn/beta" in key:
                    lowercase			:   Dict					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     ).squeeze()
              else:
                    lowercase			:   Union[str, Any]					   =		torch.from_numpy(_UpperCamelCase     )
  
              # Replace HF parameters with original TF model parameters
              hf_params[hf_key].copy_(_UpperCamelCase     )
  
  
  
  
  @torch.no_grad()
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		64
        lowercase			:   str					   =		Tokenizer(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		align.Align('''efficientnet-b7''',    '''bert-base''',    640,    _UpperCamelCase,    tok.get_vocab_size()     )
        original_model.compile()
        original_model.load_weights(_UpperCamelCase     )
  
        lowercase			:   Optional[Any]					   =		original_model.trainable_variables
        lowercase			:   Tuple					   =		original_model.non_trainable_variables
        lowercase			:   Optional[int]					   =		{param.name: param.numpy() for param in tf_params}
        for param in tf_non_train_params:
              lowercase			:   List[str]					   =		param.numpy()
        lowercase			:   str					   =		list(tf_params.keys()     )
  
        # Load HuggingFace model
        lowercase			:   Tuple					   =		get_align_config()
        lowercase			:   List[Any]					   =		AlignModel(_UpperCamelCase     ).eval()
        lowercase			:   Union[str, Any]					   =		hf_model.state_dict()
  
        # Create src-to-dst parameter name mapping dictionary
        print('''Converting parameters...'''     )
        lowercase			:   List[str]					   =		rename_keys(_UpperCamelCase     )
        replace_params(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
  
        # Initialize processor
        lowercase			:   Union[str, Any]					   =		get_processor()
        lowercase			:   Optional[Any]					   =		processor(
            images=prepare_img(),    text='''A picture of a cat''',    padding='''max_length''',    max_length=64,    return_tensors='''pt'''     )
  
        # HF model inference
        hf_model.eval()
        with torch.no_grad():
              lowercase			:   Dict					   =		hf_model(**_UpperCamelCase     )
  
        lowercase			:   int					   =		outputs.image_embeds.detach().numpy()
        lowercase			:   Any					   =		outputs.text_embeds.detach().numpy()
  
        # Original model inference
        lowercase			:   Any					   =		False
        lowercase			:   Tuple					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    do_rescale=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   str					   =		tf_image_processor(images=prepare_img(),    return_tensors='''tf''',    data_format='''channels_last'''     )['''pixel_values''']
        lowercase			:   int					   =		tok(tf.constant(['''A picture of a cat''']     )     )
  
        lowercase			:   Optional[int]					   =		original_model.image_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		original_model.text_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		tf.nn.la_normalize(_UpperCamelCase,    axis=-1     )
        lowercase			:   List[str]					   =		tf.nn.la_normalize(_UpperCamelCase,    axis=-1     )
  
        # Check whether original and HF model outputs match  -> np.allclose
        if not np.allclose(_UpperCamelCase,    _UpperCamelCase,    atol=1e-3     ):
              raise ValueError('''The predicted image features are not the same.'''     )
        if not np.allclose(_UpperCamelCase,    _UpperCamelCase,    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(_UpperCamelCase     ):
                    os.mkdir(_UpperCamelCase     )
              # Save converted model and image processor
              hf_model.save_pretrained(_UpperCamelCase     )
              processor.save_pretrained(_UpperCamelCase     )
  
        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__":
    __a   				=      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''')
  
    __a   				=      parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  