





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

lowercase_					     =	logging.get_logger(__name__)


lowercase_					     =	{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase_					     =	{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

lowercase_					     =	{"""facebook/blenderbot-3B""": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __UpperCamelCase						()       ->     Any:
  lowercase__			    =						(
      list(range(ord('!'	)	,		ord('~'	) + 1	)	) + list(range(ord('¡'	)	,		ord('¬'	) + 1	)	) + list(range(ord('®'	)	,		ord('ÿ'	) + 1	)	)
  )
  lowercase__			    =						bs[:]
  lowercase__			    =						0
  for b in range(2**8	):
    if b not in bs:
      bs.append(_SCREAMING_SNAKE_CASE	)
      cs.append(2**8 + n	)
      n += 1
  lowercase__			    =						[chr(_SCREAMING_SNAKE_CASE	) for n in cs]
  return dict(zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__			    =						set()
  lowercase__			    =						word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    lowercase__			    =						char
  return pairs


class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Optional[Any]				      = VOCAB_FILES_NAMES
   _UpperCamelCase					:	Optional[int]				      = PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase					:	int				      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase					:	Union[str, Any]				      = ['input_ids', 'attention_mask']


   def __init__(					self    :       Union[str, Any]    ,  a    :       Optional[Any]    ,  a    :       Optional[Any]    ,  a    :       str="replace"    ,  a    :       Tuple="<s>"    ,  a    :       int="</s>"    ,  a    :       Tuple="</s>"    ,  a    :       Optional[Any]="<s>"    ,  a    :       Dict="<unk>"    ,  a    :       List[Any]="<pad>"    ,  a    :       Optional[Any]="<mask>"    ,  a    :       Tuple=False    ,  **a    :       Dict    ,  )->     Any:





     """simple docstring"""







     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else bos_token
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else eos_token
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else sep_token
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else cls_token
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else unk_token
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else mask_token

     super().__init__(
         errors=a    ,  bos_token=a    ,  eos_token=a    ,  unk_token=a    ,  sep_token=a    ,  cls_token=a    ,  pad_token=a    ,  mask_token=a    ,  add_prefix_space=a    ,  **a    ,  )

     with open(a    ,  encoding='utf-8' ) as vocab_handle:
       lowercase__			    =						json.load(a )
     lowercase__			    =						{v: k for k, v in self.encoder.items()}
     lowercase__			    =						errors  # how to handle errors in decoding
     lowercase__			    =						bytes_to_unicode()
     lowercase__			    =						{v: k for k, v in self.byte_encoder.items()}
     with open(a    ,  encoding='utf-8' ) as merges_handle:
       lowercase__			    =						merges_handle.read().split('\n' )[1:-1]
     lowercase__			    =						[tuple(merge.split() ) for merge in bpe_merges]
     lowercase__			    =						dict(zip(a    ,  range(len(a ) ) ) )
     lowercase__			    =						{}
     lowercase__			    =						add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     lowercase__			    =						re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )


   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Union[str, Any]:





     """simple docstring"""







     return len(self.encoder )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     return dict(self.encoder    ,  **self.added_tokens_encoder )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       List[str] )->     Any:





     """simple docstring"""







     if token in self.cache:
       return self.cache[token]
     lowercase__			    =						tuple(a )
     lowercase__			    =						get_pairs(a )

     if not pairs:
       return token

     while True:
       lowercase__			    =						min(a    ,  key=lambda a : self.bpe_ranks.get(a    ,  float('inf' ) ) )
       if bigram not in self.bpe_ranks:
         break
       lowercase__    ,					lowercase__			    =						bigram
       lowercase__			    =						[]
       lowercase__			    =						0
       while i < len(a ):
         try:
           lowercase__			    =						word.index(a    ,  a )
         except ValueError:
           new_word.extend(word[i:] )
           break
         else:
           new_word.extend(word[i:j] )
           lowercase__			    =						j

         if word[i] == first and i < len(a ) - 1 and word[i + 1] == second:
           new_word.append(first + second )
           i += 2
         else:
           new_word.append(word[i] )
           i += 1
       lowercase__			    =						tuple(a )
       lowercase__			    =						new_word
       if len(a ) == 1:
         break
       else:
         lowercase__			    =						get_pairs(a )
     lowercase__			    =						' '.join(a )
     lowercase__			    =						word
     return word


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       Any )->     Tuple:





     """simple docstring"""







     lowercase__			    =						[]
     for token in re.findall(self.pat    ,  a ):
       lowercase__			    =						''.join(
           self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a ).split(' ' ) )
     return bpe_tokens


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[int] )->     List[str]:





     """simple docstring"""







     return self.encoder.get(a    ,  self.encoder.get(self.unk_token ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       int )->     int:





     """simple docstring"""







     return self.decoder.get(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       Any )->     List[str]:





     """simple docstring"""







     lowercase__			    =						''.join(a )
     lowercase__			    =						bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'    ,  errors=self.errors )
     return text


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       str    ,  a    :       Optional[str] = None )->     Tuple[str]:





     """simple docstring"""







     if not os.path.isdir(a ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowercase__			    =						os.path.join(
         a    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
     lowercase__			    =						os.path.join(
         a    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

     with open(a    ,  'w'    ,  encoding='utf-8' ) as f:
       f.write(json.dumps(self.encoder    ,  indent=2    ,  sort_keys=a    ,  ensure_ascii=a ) + '\n' )

     lowercase__			    =						0
     with open(a    ,  'w'    ,  encoding='utf-8' ) as writer:
       writer.write('#version: 0.2\n' )
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,  key=lambda a : kv[1] ):
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
               ' Please check that the tokenizer is not corrupted!' )
           lowercase__			    =						token_index
         writer.write(' '.join(a ) + '\n' )
         index += 1

     return vocab_file, merge_file


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None    ,  a    :       bool = False )->     List[int]:





     """simple docstring"""







     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=a    ,  token_ids_a=a    ,  already_has_special_tokens=a )

     if token_ids_a is None:
       return [1] + ([0] * len(a )) + [1]
     return [1] + ([0] * len(a )) + [1, 1] + ([0] * len(a )) + [1]


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[self.sep_token_id]
     lowercase__			    =						[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       List[str]    ,  a    :       List[Any]=False    ,  **a    :       List[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('add_prefix_space'    ,  self.add_prefix_space )
     if (is_split_into_words or add_prefix_space) and (len(a ) > 0 and not text[0].isspace()):
       lowercase__			    =						' ' + text
     return (text, kwargs)


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     Optional[Any]:





     """simple docstring"""







     return token_ids_a + [self.eos_token_id]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       "Conversation" )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[]
     for is_user, text in conversation.iter_texts():
       if is_user:
         # We need to space prefix as it's being done within blenderbot
         inputs.append(' ' + text )
       else:
         # Generated responses should contain them already.
         inputs.append(a )

     lowercase__			    =						'  '.join(a )
     lowercase__			    =						self.encode(a )
     if len(a ) > self.model_max_length:
       lowercase__			    =						input_ids[-self.model_max_length :]
       logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
     return input_ids









import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowercase_					     =	{
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						EfficientNetConfig()
  lowercase__			    =						CONFIG_MAP[model_name]['hidden_dim']
  lowercase__			    =						CONFIG_MAP[model_name]['width_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['depth_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						CONFIG_MAP[model_name]['dropout_rate']
  lowercase__			    =						CONFIG_MAP[model_name]['dw_padding']

  lowercase__			    =						'huggingface/label-files'
  lowercase__			    =						'imagenet-1k-id2label.json'
  lowercase__			    =						1000
  lowercase__			    =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		repo_type='dataset'	)	,		'r'	)	)
  lowercase__			    =						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}

  lowercase__			    =						idalabel
  lowercase__			    =						{v: k for k, v in idalabel.items()}
  return config




def     __UpperCamelCase						()       ->     Tuple:
  lowercase__			    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__			    =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,		stream=_SCREAMING_SNAKE_CASE	).raw	)
  return im




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						EfficientNetImageProcessor(
      size={'height': size, 'width': size}	,		image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,		image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	,		do_center_crop=_SCREAMING_SNAKE_CASE	,		)
  return preprocessor




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
  lowercase__			    =						sorted(set(_SCREAMING_SNAKE_CASE	)	)
  lowercase__			    =						len(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						{b: str(_SCREAMING_SNAKE_CASE	) for b, i in zip(_SCREAMING_SNAKE_CASE	,		range(_SCREAMING_SNAKE_CASE	)	)}

  lowercase__			    =						[]
  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__			    =						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""")	)

  rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
  rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
  rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
  rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
  rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

  lowercase__			    =						{}
  for item in rename_keys:
    if item[0] in original_param_names:
      lowercase__			    =						'efficientnet.' + item[1]

  lowercase__			    =						'classifier.weight'
  lowercase__			    =						'classifier.bias'
  return key_mapping




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    lowercase__			    =						key_mapping[key]
    if "_conv" in key and "kernel" in key:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	).permute(3	,		2	,		0	,		1	)
    elif "depthwise_kernel" in key:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	).permute(2	,		3	,		0	,		1	)
    elif "kernel" in key:
      lowercase__			    =						torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE	)	)
    else:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	)

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE	)




@torch.no_grad()
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						model_classes[model_name](
      include_top=_SCREAMING_SNAKE_CASE	,		weights='imagenet'	,		input_tensor=_SCREAMING_SNAKE_CASE	,		input_shape=_SCREAMING_SNAKE_CASE	,		pooling=_SCREAMING_SNAKE_CASE	,		classes=1000	,		classifier_activation='softmax'	,		)

  lowercase__			    =						original_model.trainable_variables
  lowercase__			    =						original_model.non_trainable_variables
  lowercase__			    =						{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    lowercase__			    =						param.numpy()
  lowercase__			    =						list(tf_params.keys()	)

  # Load HuggingFace model
  lowercase__			    =						get_efficientnet_config(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE	).eval()
  lowercase__			    =						hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print('Converting parameters...'	)
  lowercase__			    =						rename_keys(_SCREAMING_SNAKE_CASE	)
  replace_params(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  # Initialize preprocessor and preprocess input image
  lowercase__			    =						convert_image_processor(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						preprocessor(images=prepare_img()	,		return_tensors='pt'	)

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    lowercase__			    =						hf_model(**_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						outputs.logits.detach().numpy()

  # Original model inference
  lowercase__			    =						False
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						prepare_img().resize((image_size, image_size)	,		resample=PIL.Image.NEAREST	)
  lowercase__			    =						image.img_to_array(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						np.expand_dims(_SCREAMING_SNAKE_CASE	,		axis=0	)
  lowercase__			    =						original_model.predict(_SCREAMING_SNAKE_CASE	)

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3	), "The predicted logits are not the same."
  print('Model outputs match!'	)

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

  if push_to_hub:
    # Push model and image processor to hub
    print(F"""Pushing converted {model_name} to the hub..."""	)
    lowercase__			    =						F"""efficientnet-{model_name}"""
    preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE	)
    hf_model.push_to_hub(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""b0""",
          type=str,
          help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
      )
      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""")

      lowercase_					     =	parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


