
"""simple docstring"""







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  A__       (	_lowerCamelCase):
		A_						:       Any				   =	['image_processor', 'tokenizer']
		A_						:       Optional[Any]				   =	'AutoImageProcessor'
		A_						:       str				   =	'AutoTokenizer'

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
				    ' instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
				__lowerCAmelCase			:						Any									=      kwargs.pop('feature_extractor' )

			__lowerCAmelCase			:						str									=      image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
				raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
				raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      self.image_processor
			__lowerCAmelCase			:						Tuple									=      False

		def __call__(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			# For backward compatibility
			if self._in_target_context_manager:
				return self.current_processor(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[Any]									=      kwargs.pop('images'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      kwargs.pop('text'       ,		_SCREAMING_SNAKE_CASE )
			if len(_SCREAMING_SNAKE_CASE ) > 0:
				__lowerCAmelCase			:						Dict									=      args[0]
				__lowerCAmelCase			:						Union[str, Any]									=      args[1:]

			if images is None and text is None:
				raise ValueError('You need to specify either an `images` or `text` input to process.' )

			if images is not None:
				__lowerCAmelCase			:						Union[str, Any]									=      self.image_processor(_SCREAMING_SNAKE_CASE       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )
			if text is not None:
				__lowerCAmelCase			:						Dict									=      self.tokenizer(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if text is None:
				return inputs
			elif images is None:
				return encodings
			else:
				__lowerCAmelCase			:						Union[str, Any]									=      encodings['input_ids']
				return inputs

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		@contextmanager
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
			    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
			    'your images inputs, or in a separate call.' )
			__lowerCAmelCase			:						Any									=      True
			__lowerCAmelCase			:						Dict									=      self.tokenizer
			yield
			__lowerCAmelCase			:						Optional[int]									=      self.image_processor
			__lowerCAmelCase			:						Optional[Any]									=      False

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=False       ,		_SCREAMING_SNAKE_CASE=None ):
			if added_vocab is None:
				__lowerCAmelCase			:						str									=      self.tokenizer.get_added_vocab()

			__lowerCAmelCase			:						List[Any]									=      {}

			while tokens:
				__lowerCAmelCase			:						int									=      re.search(R'<s_(.*?)>'       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
				if start_token is None:
					break
				__lowerCAmelCase			:						Union[str, Any]									=      start_token.group(1 )
				__lowerCAmelCase			:						Tuple									=      re.search(Rf"</s_{key}>"       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
				__lowerCAmelCase			:						str									=      start_token.group()
				if end_token is None:
					__lowerCAmelCase			:						Optional[int]									=      tokens.replace(_SCREAMING_SNAKE_CASE       ,		'' )
				else:
					__lowerCAmelCase			:						Optional[Any]									=      end_token.group()
					__lowerCAmelCase			:						Tuple									=      re.escape(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Tuple									=      re.escape(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Dict									=      re.search(f"{start_token_escaped}(.*?){end_token_escaped}"       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
					if content is not None:
						__lowerCAmelCase			:						List[str]									=      content.group(1 ).strip()
						if r"<s_" in content and r"</s_" in content:  # non-leaf node
							__lowerCAmelCase			:						int									=      self.tokenajson(_SCREAMING_SNAKE_CASE       ,		is_inner_value=_SCREAMING_SNAKE_CASE       ,		added_vocab=_SCREAMING_SNAKE_CASE )
							if value:
								if len(_SCREAMING_SNAKE_CASE ) == 1:
									__lowerCAmelCase			:						Tuple									=      value[0]
								__lowerCAmelCase			:						Tuple									=      value
						else:  # leaf nodes
							__lowerCAmelCase			:						Any									=      []
							for leaf in content.split(R'<sep/>' ):
								__lowerCAmelCase			:						List[Any]									=      leaf.strip()
								if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
									__lowerCAmelCase			:						Dict									=      leaf[1:-2]  # for categorical special tokens
								output[key].append(_SCREAMING_SNAKE_CASE )
							if len(output[key] ) == 1:
								__lowerCAmelCase			:						str									=      output[key][0]

					__lowerCAmelCase			:						Dict									=      tokens[tokens.find(_SCREAMING_SNAKE_CASE ) + len(_SCREAMING_SNAKE_CASE ) :].strip()
					if tokens[:6] == r"<sep/>":  # non-leaf nodes
						return [output] + self.tokenajson(tokens[6:]       ,		is_inner_value=_SCREAMING_SNAKE_CASE       ,		added_vocab=_SCREAMING_SNAKE_CASE )

			if len(_SCREAMING_SNAKE_CASE ):
				return [output] if is_inner_value else output
			else:
				return [] if is_inner_value else {"text_sequence": tokens}

		@property
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
			return self.image_processor_class







		@property
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
			return self.image_processor




"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  Optional[int]												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
			    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
			    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       func(
									    __snake_case					,**__snake_case	)

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


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