

import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


lowerCAmelCase       :				List[str]         =					logging.get_logger(__name__)


lowerCAmelCase       :				Any         =					{
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

lowerCAmelCase       :				int         =					'\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'
class 	_A						(					__magic_name__):
		SCREAMING_SNAKE_CASE      : Dict    =					'''bark_module'''
		SCREAMING_SNAKE_CASE      : str    =					['''past_key_values''']

		SCREAMING_SNAKE_CASE      : str    =					{
		    '''num_attention_heads''': '''num_heads''',
		    '''num_hidden_layers''': '''num_layers''',
		    '''vocab_size''': '''input_vocab_size''',
		    '''window_size''': '''block_size''',
		}

		def __init__(			self	,   _SCREAMING_SNAKE_CASE=1024	,   _SCREAMING_SNAKE_CASE=1_0048	,   _SCREAMING_SNAKE_CASE=1_0048	,   _SCREAMING_SNAKE_CASE=12	,   _SCREAMING_SNAKE_CASE=12	,   _SCREAMING_SNAKE_CASE=768	,   _SCREAMING_SNAKE_CASE=0.0	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=0.02	,   _SCREAMING_SNAKE_CASE=True	,   **_SCREAMING_SNAKE_CASE	,   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       block_size
								SCREAMING_SNAKE_CASE_			:					List[str]          =       input_vocab_size
								SCREAMING_SNAKE_CASE_			:					str          =       output_vocab_size
								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       num_layers
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       num_heads
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       hidden_size
								SCREAMING_SNAKE_CASE_			:					Any          =       dropout
								SCREAMING_SNAKE_CASE_			:					Dict          =       bias
								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       use_cache
								SCREAMING_SNAKE_CASE_			:					int          =       initializer_range

								super().__init__(**_SCREAMING_SNAKE_CASE   )



		@classmethod
		def 	UpperCAmelCase      (			cls	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = "main"	,   **_SCREAMING_SNAKE_CASE	,   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Tuple          =       cache_dir
								SCREAMING_SNAKE_CASE_			:					str          =       force_download
								SCREAMING_SNAKE_CASE_			:					Tuple          =       local_files_only
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       revision

								cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

								SCREAMING_SNAKE_CASE_			:					Any          =       cls.get_config_dict(_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

								# get the config dict if we are loading from Bark
								if config_dict.get('model_type'   ) == "bark":
														SCREAMING_SNAKE_CASE_			:					str          =       config_dict[f"{cls.model_type}_config"]

								if "model_type" in config_dict and hasattr(cls	,   'model_type'   ) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
														    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."   )

								return cls.from_dict(_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkSemanticConfig'''				,       model='''BarkSemanticModel''')				,       '''
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''				,       )
class 	_A						(					__magic_name__):
		SCREAMING_SNAKE_CASE      : Union[str, Any]    =					'''semantic'''

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''				,       model='''BarkCoarseModel''')				,       '''
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''				,       )
class 	_A						(					__magic_name__):
		SCREAMING_SNAKE_CASE      : Any    =					'''coarse_acoustics'''

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''				,       model='''BarkFineModel''')				,       '''
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''				,       )
class 	_A						(					__magic_name__):
		SCREAMING_SNAKE_CASE      : List[Any]    =					'''fine_acoustics'''

		def __init__(			self	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=8	,   _SCREAMING_SNAKE_CASE=1	,   **_SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       n_codes_total
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       n_codes_given

								super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

class 	_A						(					__magic_name__):
		SCREAMING_SNAKE_CASE      : Any    =					'''bark'''
		SCREAMING_SNAKE_CASE      : Dict    =					True

		def __init__(			self	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE=0.02	,   **_SCREAMING_SNAKE_CASE	,   ):





								"""simple docstring"""







								if semantic_config is None:
														SCREAMING_SNAKE_CASE_			:					str          =       {}
														logger.info('semantic_config is None. initializing the semantic model with default values.'   )

								if coarse_acoustics_config is None:
														SCREAMING_SNAKE_CASE_			:					str          =       {}
														logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'   )

								if fine_acoustics_config is None:
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       {}
														logger.info('fine_acoustics_config is None. initializing the fine model with default values.'   )

								if codec_config is None:
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       {}
														logger.info('codec_config is None. initializing the codec model with default values.'   )

								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       BarkSemanticConfig(**_SCREAMING_SNAKE_CASE   )
								SCREAMING_SNAKE_CASE_			:					Dict          =       BarkCoarseConfig(**_SCREAMING_SNAKE_CASE   )
								SCREAMING_SNAKE_CASE_			:					Tuple          =       BarkFineConfig(**_SCREAMING_SNAKE_CASE   )
								SCREAMING_SNAKE_CASE_			:					List[str]          =       AutoConfig.for_model(**_SCREAMING_SNAKE_CASE   )

								SCREAMING_SNAKE_CASE_			:					Tuple          =       initializer_range

								super().__init__(**_SCREAMING_SNAKE_CASE   )

		@classmethod
		def 	UpperCAmelCase      (			cls	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE	,   ):





								"""simple docstring"""







								return cls(
								    semantic_config=semantic_config.to_dict()	,   coarse_acoustics_config=coarse_acoustics_config.to_dict()	,   fine_acoustics_config=fine_acoustics_config.to_dict()	,   codec_config=codec_config.to_dict()	,   **_SCREAMING_SNAKE_CASE	,   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					str          =       copy.deepcopy(self.__dict__   )

								SCREAMING_SNAKE_CASE_			:					List[str]          =       self.semantic_config.to_dict()
								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.coarse_acoustics_config.to_dict()
								SCREAMING_SNAKE_CASE_			:					List[str]          =       self.fine_acoustics_config.to_dict()
								SCREAMING_SNAKE_CASE_			:					List[str]          =       self.codec_config.to_dict()

								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.__class__.model_type
								return output









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)






