



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 	lowerCamelCase_    ( lowercase_						):






						'''simple docstring'''
						a__   =  ["image_processor", "tokenizer"]
						a__   =  "ViltImageProcessor"
						a__   =  ("BertTokenizer", "BertTokenizerFast")







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Dict=None	,	__lowerCamelCase      :			Optional[int]=None	,	**__lowerCamelCase      :			List[str] )      ->				Optional[Any]:
									A							:					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."	,	lowerCamelCase_	,	)
												A							:					Union[str, Any]              =						kwargs.pop("feature_extractor" )

									A							:					List[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__(lowerCamelCase_	,	lowerCamelCase_ )
									A							:					Dict              =						self.image_processor







						def __call__(	self      :			List[str]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Union[bool, str, PaddingStrategy] = False	,	__lowerCamelCase      :			Union[bool, str, TruncationStrategy] = None	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			int = 0	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Optional[Union[str, TensorType]] = None	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				int:
									A							:					List[str]              =						self.tokenizer(
									    text=lowerCamelCase_	,	add_special_tokens=lowerCamelCase_	,	padding=lowerCamelCase_	,	truncation=lowerCamelCase_	,	max_length=lowerCamelCase_	,	stride=lowerCamelCase_	,	pad_to_multiple_of=lowerCamelCase_	,	return_token_type_ids=lowerCamelCase_	,	return_attention_mask=lowerCamelCase_	,	return_overflowing_tokens=lowerCamelCase_	,	return_special_tokens_mask=lowerCamelCase_	,	return_offsets_mapping=lowerCamelCase_	,	return_length=lowerCamelCase_	,	verbose=lowerCamelCase_	,	return_tensors=lowerCamelCase_	,	**lowerCamelCase_	,	)
									# add pixel_values + pixel_mask
									A							:					Dict              =						self.image_processor(lowerCamelCase_	,	return_tensors=lowerCamelCase_ )
									encoding.update(lowerCamelCase_ )

									return encoding







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			Optional[int] )      ->				Dict:
									return self.tokenizer.batch_decode(*lowerCamelCase_	,	**lowerCamelCase_ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	*__lowerCamelCase      :			int	,	**__lowerCamelCase      :			Any )      ->				str:
									return self.tokenizer.decode(*lowerCamelCase_	,	**lowerCamelCase_ )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				str:
									A							:					List[Any]              =						self.tokenizer.model_input_names
									A							:					List[str]              =						self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[str]:
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,	lowerCamelCase_	,	)
									return self.image_processor










import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Optional[int]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Optional[Any] )      ->				bool:
									raise NotImplementedError("StoppingCriteria needs to be subclassed" )







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int] = None )      ->				List[Any]:
									A							:					str              =						max_length
									A							:					Optional[int]              =						max_position_embeddings







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			List[str]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Any )      ->				bool:
									A							:					List[Any]              =						input_ids.shape[-1]
									A							:					Any              =						cur_len >= self.max_length
									if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
												logger.warning_once(
												    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
												    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
												    "exceptions, performance degradation, or nothing at all." )
									return is_done







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			int )      ->				List[Any]:
									warnings.warn(
									    "The class `MaxNewTokensCriteria` is deprecated. "
									    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
									    "with `max_length = start_length + max_new_tokens` instead."	,	__lowerCamelCase	,	)
									A							:					str              =						start_length
									A							:					Optional[Any]              =						max_new_tokens
									A							:					Dict              =						start_length + max_new_tokens







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			int	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return input_ids.shape[-1] >= self.max_length







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			float	,	__lowerCamelCase      :			Optional[float] = None )      ->				List[Any]:
									A							:					str              =						max_time
									A							:					Dict              =						time.time() if initial_timestamp is None else initial_timestamp







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Any	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return time.time() - self.initial_timestamp > self.max_time







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			int )      ->				bool:
									return any(criteria(__lowerCamelCase	,	__lowerCamelCase ) for criteria in self )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									for stopping_criterium in self:
												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
												elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
									return None







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[int]              =						stopping_criteria.max_length
			A							:					Any              =						deepcopy(_lowerCamelCase    )
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter" ,    _lowerCamelCase    )
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCamelCase    )    )
			return new_stopping_criteria