


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__			:      str								=logging.get_logger(__name__)


lowerCAmelCase__			:      Any								={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__			:      Union[str, Any]								={
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase__			:      Tuple								={'facebook/blenderbot_small-90M': 5_12}






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					set()
			SCREAMING_SNAKE_CASE_		:     int					=					word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)				)
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					char

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					set(A__				)
			return pairs







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						VOCAB_FILES_NAMES
							_UpperCAmelCase      =						PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      =						["""input_ids""", """attention_mask"""]






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="__start__"			,						lowerCAmelCase__="__end__"			,						lowerCAmelCase__="__unk__"			,						lowerCAmelCase__="__null__"			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(unk_token=lowerCAmelCase__			,						bos_token=lowerCAmelCase__			,						eos_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						**lowerCAmelCase__       )

										with open(lowerCAmelCase__			,						encoding='utf-8'       ) as vocab_handle:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					json.load(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{v: k for k, v in self.encoder.items()}
										with open(lowerCAmelCase__			,						encoding='utf-8'       ) as merges_handle:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					merges_handle.read().split('\n'       )[1:-1]
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[tuple(merge.split()       ) for merge in merges]
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{}






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return len(self.encoder       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if token in self.cache:
													return self.cache[token]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					re.sub('([.,!?()])'			,						r' \1'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					re.sub('(\')'			,						r' \1 '			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					re.sub(r'\s{2,}'			,						' '			,						lowerCAmelCase__       )
										if "\n" in token:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					token.replace('\n'			,						' __newln__'       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					token.split(' '       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
										for token in tokens:
													if not len(lowerCAmelCase__       ):
																continue

													SCREAMING_SNAKE_CASE_		:     Any					=					token.lower()
													SCREAMING_SNAKE_CASE_		:     Any					=					tuple(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					tuple(list(word[:-1]       ) + [word[-1] + '</w>']       )
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					get_pairs(lowerCAmelCase__       )

													if not pairs:
																words.append(lowerCAmelCase__       )
																continue

													while True:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					min(lowerCAmelCase__			,						key=lambda lowerCAmelCase__       : self.bpe_ranks.get(lowerCAmelCase__			,						float('inf'       )       )       )
																if bigram not in self.bpe_ranks:
																			break
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					bigram
																SCREAMING_SNAKE_CASE_		:     str					=					[]
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					0

																while i < len(lowerCAmelCase__       ):
																			try:
																						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					word.index(lowerCAmelCase__			,						lowerCAmelCase__       )
																						new_word.extend(word[i:j]       )
																						SCREAMING_SNAKE_CASE_		:     List[Any]					=					j
																			except ValueError:
																						new_word.extend(word[i:]       )
																						break

																			if word[i] == first and i < len(lowerCAmelCase__       ) - 1 and word[i + 1] == second:
																						new_word.append(first + second       )
																						i += 2
																			else:
																						new_word.append(word[i]       )
																						i += 1
																SCREAMING_SNAKE_CASE_		:     Dict					=					tuple(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					new_word
																if len(lowerCAmelCase__       ) == 1:
																			break
																else:
																			SCREAMING_SNAKE_CASE_		:     str					=					get_pairs(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					'@@ '.join(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					word[:-4]

													SCREAMING_SNAKE_CASE_		:     Tuple					=					word
													words.append(lowerCAmelCase__       )
										return " ".join(lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					re.findall(r'\S+\n?'			,						lowerCAmelCase__       )

										for token in words:
													split_tokens.extend(list(self.bpe(lowerCAmelCase__       ).split(' '       )       )       )
										return split_tokens






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					token.lower()
										return self.encoder.get(lowerCAmelCase__			,						self.encoder.get(self.unk_token       )       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										return self.decoder.get(lowerCAmelCase__			,						self.unk_token       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					' '.join(lowerCAmelCase__       ).replace('@@ '			,						''       ).strip()
										return out_string






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										if not os.path.isdir(lowerCAmelCase__       ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										SCREAMING_SNAKE_CASE_		:     int					=					os.path.join(
										    lowerCAmelCase__			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					os.path.join(
										    lowerCAmelCase__			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

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

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					0
										with open(lowerCAmelCase__			,						'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 lowerCAmelCase__       : 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!'       )
																			SCREAMING_SNAKE_CASE_		:     Tuple					=					token_index
																writer.write(' '.join(lowerCAmelCase__       ) + '\n'       )
																index += 1

										return vocab_file, merge_file











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        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''image_processor''', '''tokenizer''']
			snake_case_  						=			'''OwlViTImageProcessor'''
			snake_case_  						=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


			def __init__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'     ,		lowerCamelCase__     ,		)
											__lowerCamelCase									=       kwargs.pop('feature_extractor'				)

							__lowerCamelCase									=       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__(lowerCamelCase__     ,		lowerCamelCase__				)


			def __call__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__="max_length"     ,		lowerCamelCase__="np"     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''

							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(lowerCamelCase__     ,		lowerCamelCase__				) or (isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and not isinstance(text[0]     ,		lowerCamelCase__				)):
															__lowerCamelCase									=       [self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)]

											elif isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and isinstance(text[0]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       []

															# Maximum number of queries across batch
															__lowerCamelCase									=       max([len(lowerCamelCase__				) for t in text]				)

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

																			__lowerCamelCase									=       self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)
																			encodings.append(lowerCamelCase__				)
											else:
															raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

											if return_tensors == "np":
															__lowerCamelCase									=       np.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       np.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

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

															__lowerCamelCase									=       jnp.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

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

															__lowerCamelCase									=       torch.cat([encoding['input_ids'] for encoding in encodings]     ,		dim=0				)
															__lowerCamelCase									=       torch.cat([encoding['attention_mask'] for encoding in encodings]     ,		dim=0				)

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

															__lowerCamelCase									=       tf.stack([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       tf.stack([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

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

											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       input_ids
											__lowerCamelCase									=       attention_mask

							if query_images is not None:
											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       self.image_processor(
											    lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				).pixel_values
											__lowerCamelCase									=       query_pixel_values

							if images is not None:
											__lowerCamelCase									=       self.image_processor(lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)

							if text is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif text is not None or query_images is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowerCamelCase__				)     ,		tensor_type=lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							return self.image_processor.post_process(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor_class






			@property
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor


