





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase_					     =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	str				      = field(
       default=__SCREAMING_SNAKE_CASE    ,			metadata={'help': 'Model type selected in the list: ' + ', '.join(__SCREAMING_SNAKE_CASE   )}   )
   _UpperCamelCase					:	str				      = field(
       default=__SCREAMING_SNAKE_CASE    ,			metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}   )
   _UpperCamelCase					:	int				      = field(
       default=1_28    ,			metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }    ,			)
   _UpperCamelCase					:	int				      = field(
       default=1_28    ,			metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}    ,			)
   _UpperCamelCase					:	int				      = field(
       default=64    ,			metadata={
           'help': (
               'The maximum number of tokens for the question. Questions longer than this will '
               'be truncated to this length.'
           )
       }    ,			)
   _UpperCamelCase					:	int				      = field(
       default=30    ,			metadata={
           'help': (
               'The maximum length of an answer that can be generated. This is needed because the start '
               'and end predictions are not conditioned on one another.'
           )
       }    ,			)
   _UpperCamelCase					:	bool				      = field(
       default=__SCREAMING_SNAKE_CASE    ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
   _UpperCamelCase					:	bool				      = field(
       default=__SCREAMING_SNAKE_CASE    ,			metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}   )
   _UpperCamelCase					:	float				      = field(
       default=0.0    ,			metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
   _UpperCamelCase					:	int				      = field(
       default=20    ,			metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
   _UpperCamelCase					:	int				      = field(
       default=0    ,			metadata={
           'help': (
               'language id of input for language-specific xlm models (see'
               ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
           )
       }    ,			)
   _UpperCamelCase					:	int				      = field(default=1    ,			metadata={'help': 'multiple threads for converting example to features'}   )






class        SCREAMING_SNAKE_CASE     (__SCREAMING_SNAKE_CASE   ):
   _UpperCamelCase					:	Union[str, Any]				      = 'train'
   _UpperCamelCase					:	List[Any]				      = 'dev'






class        SCREAMING_SNAKE_CASE     (__SCREAMING_SNAKE_CASE   ):
   _UpperCamelCase					:	SquadDataTrainingArguments
   _UpperCamelCase					:	List[SquadFeatures]
   _UpperCamelCase					:	Split
   _UpperCamelCase					:	bool


   def __init__(					self    :       Dict    ,  a    :       Union[str, Any]    ,  a    :       int    ,  a    :       Any = None    ,  a    :       int = Split.train    ,  a    :       Optional[Any] = False    ,  a    :       Optional[Any] = None    ,  a    :       Any = "pt"    ,  )->     Any:





     """simple docstring"""







     lowercase__			    =						args
     lowercase__			    =						is_language_sensitive
     lowercase__			    =						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
     if isinstance(_a    ,  _a ):
       try:
         lowercase__			    =						Split[mode]
       except KeyError:
         raise KeyError('mode is not a valid split name' )
     lowercase__			    =						mode
     # Load data features from cache or dataset file
     lowercase__			    =						'v2' if args.version_2_with_negative else 'v1'
     lowercase__			    =						os.path.join(
         cache_dir if cache_dir is not None else args.data_dir    ,  f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""    ,  )

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     lowercase__			    =						cached_features_file + '.lock'
     with FileLock(_a ):
       if os.path.exists(_a ) and not args.overwrite_cache:
         lowercase__			    =						time.time()
         lowercase__			    =						torch.load(_a )

         # Legacy cache files have only features, while new cache files
         # will have dataset and examples also.
         lowercase__			    =						self.old_features['features']
         lowercase__			    =						self.old_features.get('dataset'    ,  _a )
         lowercase__			    =						self.old_features.get('examples'    ,  _a )
         logger.info(
             f"""Loading features from cached file {cached_features_file} [took %.3f s]"""    ,  time.time() - start )

         if self.dataset is None or self.examples is None:
           logger.warning(
               f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
               ' future run' )
       else:
         if mode == Split.dev:
           lowercase__			    =						self.processor.get_dev_examples(args.data_dir )
         else:
           lowercase__			    =						self.processor.get_train_examples(args.data_dir )

         lowercase__    ,					lowercase__			    =						squad_convert_examples_to_features(
             examples=self.examples    ,  tokenizer=_a    ,  max_seq_length=args.max_seq_length    ,  doc_stride=args.doc_stride    ,  max_query_length=args.max_query_length    ,  is_training=mode == Split.train    ,  threads=args.threads    ,  return_dataset=_a    ,  )

         lowercase__			    =						time.time()
         torch.save(
             {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}    ,  _a    ,  )
         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
         logger.info(
             f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )


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





     """simple docstring"""







     return len(self.features )


   def __getitem__(					self    :       int    ,  a    :       List[Any] )->     Any:





     """simple docstring"""







     lowercase__			    =						self.features[i]

     lowercase__			    =						torch.tensor(feature.input_ids    ,  dtype=torch.long )
     lowercase__			    =						torch.tensor(feature.attention_mask    ,  dtype=torch.long )
     lowercase__			    =						torch.tensor(feature.token_type_ids    ,  dtype=torch.long )
     lowercase__			    =						torch.tensor(feature.cls_index    ,  dtype=torch.long )
     lowercase__			    =						torch.tensor(feature.p_mask    ,  dtype=torch.float )
     lowercase__			    =						torch.tensor(feature.is_impossible    ,  dtype=torch.float )

     lowercase__			    =						{
         'input_ids': input_ids,
         'attention_mask': attention_mask,
         'token_type_ids': token_type_ids,
     }

     if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
       del inputs["token_type_ids"]

     if self.args.model_type in ["xlnet", "xlm"]:
       inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
       if self.args.version_2_with_negative:
         inputs.update({'is_impossible': is_impossible} )
       if self.is_language_sensitive:
         inputs.update({'langs': (torch.ones(input_ids.shape    ,  dtype=torch.intaa ) * self.args.lang_id)} )

     if self.mode == Split.train:
       lowercase__			    =						torch.tensor(feature.start_position    ,  dtype=torch.long )
       lowercase__			    =						torch.tensor(feature.end_position    ,  dtype=torch.long )
       inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )

     return inputs









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)


