"""simple docstring"""





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    List[Any]   =  ["image_processor", "tokenizer"]
      lowerCamelCase__		:    Union[str, Any]   =  "OwlViTImageProcessor"
      lowerCamelCase__		:    Tuple   =  ("CLIPTokenizer", "CLIPTokenizerFast")


      def __init__(   self		,			a=None		,			a=None		,			**a							)       ->					Optional[int]:
             lowercase__			:		List[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.'		,			a		,			)
                    lowercase__			:		List[Any]       =	kwargs.pop('feature_extractor'							)

             lowercase__			:		int       =	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__(a		,			a							)


      def __call__(   self		,			a=None		,			a=None		,			a=None		,			a="max_length"		,			a="np"		,			**a							)       ->					List[Any]:

             if text is None and query_images is None and images is None:
                    raise ValueError(
                        'You have to specify at least one text or query image or image. All three cannot be none.'							)

             if text is not None:
                    if isinstance(a		,			a							) or (isinstance(a		,			a							) and not isinstance(text[0]		,			a							)):
                           lowercase__			:		int       =	[self.tokenizer(a		,			padding=a		,			return_tensors=a		,			**a							)]

                    elif isinstance(a		,			a							) and isinstance(text[0]		,			a							):
                           lowercase__			:		Union[str, Any]       =	[]

                           # Maximum number of queries across batch
                           lowercase__			:		str       =	max([len(a							) for t in text]							)

                           # Pad all batch samples to max number of text queries
                           for t in text:
                                  if len(a							) != max_num_queries:
                                         lowercase__			:		Optional[int]       =	t + [' '] * (max_num_queries - len(a							))

                                  lowercase__			:		Union[str, Any]       =	self.tokenizer(a		,			padding=a		,			return_tensors=a		,			**a							)
                                  encodings.append(a							)
                    else:
                           raise TypeError('Input text should be a string, a list of strings or a nested list of strings'							)

                    if return_tensors == "np":
                           lowercase__			:		List[str]       =	np.concatenate([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Union[str, Any]       =	np.concatenate([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    elif return_tensors == "jax" and is_flax_available():
                           import jax.numpy as jnp

                           lowercase__			:		List[str]       =	jnp.concatenate([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Optional[int]       =	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    elif return_tensors == "pt" and is_torch_available():
                           import torch

                           lowercase__			:		Dict       =	torch.cat([encoding['input_ids'] for encoding in encodings]		,			dim=0							)
                           lowercase__			:		Tuple       =	torch.cat([encoding['attention_mask'] for encoding in encodings]		,			dim=0							)

                    elif return_tensors == "tf" and is_tf_available():
                           import tensorflow as tf

                           lowercase__			:		str       =	tf.stack([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Tuple       =	tf.stack([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    else:
                           raise ValueError('Target return tensor type could not be returned'							)

                    lowercase__			:		Dict       =	BatchEncoding()
                    lowercase__			:		str       =	input_ids
                    lowercase__			:		Dict       =	attention_mask

             if query_images is not None:
                    lowercase__			:		Union[str, Any]       =	BatchEncoding()
                    lowercase__			:		Any       =	self.image_processor(
                        a		,			return_tensors=a		,			**a							).pixel_values
                    lowercase__			:		str       =	query_pixel_values

             if images is not None:
                    lowercase__			:		List[Any]       =	self.image_processor(a		,			return_tensors=a		,			**a							)

             if text is not None and images is not None:
                    lowercase__			:		str       =	image_features.pixel_values
                    return encoding
             elif query_images is not None and images is not None:
                    lowercase__			:		List[Any]       =	image_features.pixel_values
                    return encoding
             elif text is not None or query_images is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**a							)		,			tensor_type=a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Tuple:
             return self.image_processor.post_process(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Dict:
             return self.image_processor.post_process_object_detection(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Optional[Any]:
             return self.image_processor.post_process_image_guided_detection(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Optional[int]:
             return self.tokenizer.batch_decode(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Union[str, Any]:
             return self.tokenizer.decode(*a		,			**a							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			a		,			)
             return self.image_processor_class


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,			a		,			)
             return self.image_processor





"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			set()

		# Replace all the whitespace in our sentence
		__a           =			input_str.replace(''' '''    ,					''''''			)
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()			)
		return len(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			[False] * 26
		for char in input_str:
				if char.islower():
						__a           =			True
				elif char.isupper():
						__a           =			True
		return all(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
		print(timeit('''is_pangram()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_faster()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_fastest()'''    ,					setup=lowerCAmelCase__			)			)
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()






