



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import BridgeTowerImageProcessor

class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def __init__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Dict[str, int] = None	,	__lowerCamelCase      :			int = 32	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Union[int, float] = 1 / 2_55	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]	,	__lowerCamelCase      :			Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Any=7	,	__lowerCamelCase      :			Dict=30	,	__lowerCamelCase      :			Any=4_00	,	__lowerCamelCase      :			int=3	,	)      ->				Optional[int]:
									A							:					Tuple              =						parent
									A							:					int              =						do_resize
									A							:					List[str]              =						size if size is not None else {"shortest_edge": 2_88}
									A							:					Tuple              =						size_divisor
									A							:					List[str]              =						do_rescale
									A							:					str              =						rescale_factor
									A							:					Optional[int]              =						do_normalize
									A							:					Union[str, Any]              =						do_center_crop
									A							:					str              =						image_mean
									A							:					Tuple              =						image_std
									A							:					Any              =						do_pad
									A							:					Optional[int]              =						batch_size
									A							:					Union[str, Any]              =						num_channels
									A							:					str              =						min_resolution
									A							:					int              =						max_resolution







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any]=False )      ->				Union[str, Any]:
									if not batched:
												A							:					int              =						self.size["shortest_edge"]
												A							:					int              =						image_inputs[0]
												if isinstance(__lowerCamelCase	,	Image.Image ):
															A       , A							:					Tuple              =						image.size
												else:
															A       , A							:					List[str]              =						image.shape[1], image.shape[2]
												A							:					str              =						size / min(__lowerCamelCase	,	__lowerCamelCase )
												if h < w:
															A       , A							:					int              =						size, scale * w
												else:
															A       , A							:					Optional[int]              =						scale * h, size

												A							:					str              =						int((13_33 / 8_00) * size )
												if max(__lowerCamelCase	,	__lowerCamelCase ) > max_size:
															A							:					Optional[int]              =						max_size / max(__lowerCamelCase	,	__lowerCamelCase )
															A							:					Union[str, Any]              =						newh * scale
															A							:					Optional[Any]              =						neww * scale

												A       , A							:					Dict              =						int(newh + 0.5 ), int(neww + 0.5 )
												A       , A							:					List[Any]              =						(
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

									else:
												A							:					List[str]              =						[]
												for image in image_inputs:
															A       , A							:					List[Any]              =						self.get_expected_values([image] )
															expected_values.append((expected_height, expected_width) )
												A							:					Any              =						max(__lowerCamelCase	,	key=lambda __lowerCamelCase : item[0] )[0]
												A							:					Optional[int]              =						max(__lowerCamelCase	,	key=lambda __lowerCamelCase : item[1] )[1]

									return expected_height, expected_width







@require_torch
@require_vision
class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  BridgeTowerImageProcessor if is_vision_available() else None







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Tuple:
									A							:					Union[str, Any]              =						BridgeTowerImageProcessingTester(self )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[str]:
									return self.image_processor_tester.prepare_image_processor_dict()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									A							:					Optional[Any]              =						self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(__lowerCamelCase	,	"image_mean" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"image_std" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"do_normalize" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"do_resize" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"size" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"size_divisor" ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									# Initialize image processor
									A							:					Any              =						self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									A							:					Optional[Any]              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	Image.Image )

									# Test not batched input
									A							:					Optional[int]              =						image_processing(image_inputs[0]	,	return_tensors="pt" ).pixel_values

									A       , A							:					str              =						self.image_processor_tester.get_expected_values(__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

									# Test batched
									A							:					Optional[Any]              =						image_processing(__lowerCamelCase	,	return_tensors="pt" ).pixel_values

									A       , A							:					Optional[int]              =						self.image_processor_tester.get_expected_values(__lowerCamelCase	,	batched=__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Union[str, Any]:
									# Initialize image processor
									A							:					int              =						self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									A							:					int              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase	,	numpify=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	np.ndarray )

									# Test not batched input
									A							:					List[str]              =						image_processing(image_inputs[0]	,	return_tensors="pt" ).pixel_values

									A       , A							:					Optional[int]              =						self.image_processor_tester.get_expected_values(__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

									# Test batched
									A							:					Any              =						image_processing(__lowerCamelCase	,	return_tensors="pt" ).pixel_values

									A       , A							:					List[Any]              =						self.image_processor_tester.get_expected_values(__lowerCamelCase	,	batched=__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									# Initialize image processor
									A							:					List[str]              =						self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									A							:					List[str]              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase	,	torchify=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	torch.Tensor )

									# Test not batched input
									A							:					Any              =						image_processing(image_inputs[0]	,	return_tensors="pt" ).pixel_values

									A       , A							:					List[str]              =						self.image_processor_tester.get_expected_values(__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

									# Test batched
									A							:					str              =						image_processing(__lowerCamelCase	,	return_tensors="pt" ).pixel_values

									A       , A							:					str              =						self.image_processor_tester.get_expected_values(__lowerCamelCase	,	batched=__lowerCamelCase )
									self.assertEqual(
									    encoded_images.shape	,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )	,	)




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