


# 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





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)

