


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git_vision_model"""




      def __init__(    self:	Tuple						,				a:	Tuple=768						,				a:	Optional[int]=3072						,				a:	Dict=12						,				a:	Tuple=12						,				a:	Optional[int]=3						,				a:	Any=224						,				a:	Dict=16						,				a:	Optional[int]="quick_gelu"						,				a:	Tuple=1e-5						,				a:	Dict=0.0						,				a:	List[Any]=0.0_2						,				**a:	List[str]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							List[str]		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Optional[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							str		 =						num_attention_heads
       __lowerCamelCase		:							List[str]		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						patch_size
       __lowerCamelCase		:							str		 =						image_size
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Union[str, Any]		 =						attention_dropout
       __lowerCamelCase		:							str		 =						layer_norm_eps
       __lowerCamelCase		:							Optional[Any]		 =						hidden_act




      @classmethod
      def 					_snake_case     (    cls:	Union[str, Any]						,				a:	Union[str, os.PathLike]						,				**a:	Union[str, Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from GITConfig
       if config_dict.get('model_type'    ) == "git":
        __lowerCamelCase		:							str		 =						config_dict['vision_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(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git"""




      def __init__(    self:	List[Any]						,				a:	Tuple=None						,				a:	Any=3_0522						,				a:	List[Any]=768						,				a:	Union[str, Any]=6						,				a:	Tuple=12						,				a:	Union[str, Any]=3072						,				a:	List[Any]="gelu"						,				a:	Optional[int]=0.1						,				a:	str=0.1						,				a:	str=1024						,				a:	List[Any]=0.0_2						,				a:	Optional[Any]=1e-12						,				a:	Tuple=0						,				a:	Dict="absolute"						,				a:	List[str]=True						,				a:	Tuple=False						,				a:	int=101						,				a:	Any=102						,				a:	Union[str, Any]=None						,				**a:	Any						,				):
       super().__init__(bos_token_id=a						,				eos_token_id=a						,				pad_token_id=a						,				**a    )

       if vision_config is None:
        __lowerCamelCase		:							List[Any]		 =						{}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.'    )

       __lowerCamelCase		:							str		 =						GitVisionConfig(**a    )
       __lowerCamelCase		:							Any		 =						vocab_size
       __lowerCamelCase		:							List[Any]		 =						hidden_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Dict		 =						hidden_act
       __lowerCamelCase		:							Optional[int]		 =						intermediate_size
       __lowerCamelCase		:							Tuple		 =						hidden_dropout_prob
       __lowerCamelCase		:							Dict		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							List[Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						layer_norm_eps
       __lowerCamelCase		:							Dict		 =						position_embedding_type
       __lowerCamelCase		:							Optional[int]		 =						use_cache
       __lowerCamelCase		:							int		 =						tie_word_embeddings
       __lowerCamelCase		:							str		 =						num_image_with_embedding

       __lowerCamelCase		:							Optional[int]		 =						bos_token_id
       __lowerCamelCase		:							List[Any]		 =						eos_token_id




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							List[str]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Optional[int]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							int		 =						self.__class__.model_type
       return output





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						dataset.map(**SCREAMING_SNAKE_CASE__						)
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						dataset.filter(**SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCamelCase		:							Any		 =						datasets.Features({'text': datasets.Value('string'						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							Any		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							List[Any]		 =						transformers.AutoTokenizer.from_pretrained('bert-base-cased'      ,	use_fast=SCREAMING_SNAKE_CASE__						)

  def tokenize(SCREAMING_SNAKE_CASE__						):
   return tokenizer(examples['text']						)

  __lowerCamelCase		:							str		 =						map(SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							int		 =						map(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='numpy'						):
   __lowerCamelCase		:							Union[str, Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='pandas'						):
   __lowerCamelCase		:							Any		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='torch'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[str]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='tensorflow'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						filter(SCREAMING_SNAKE_CASE__						)

  # Activate later when tokenizer support batched inputs
  # with dataset.formatted_as(type='numpy'):
  #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

 with open(SCREAMING_SNAKE_CASE__      ,	'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()

