'''simple docstring'''

import unittest

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 ViltImageProcessor




class 		SCREAMING_SNAKE_CASE_				(unittest.TestCase			):



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_=7							, snake_case_=3							, snake_case_=18							, snake_case_=30							, snake_case_=400							, snake_case_=True							, snake_case_=None							, snake_case_=2							, snake_case_=True							, snake_case_=[0.5, 0.5, 0.5]							, snake_case_=[0.5, 0.5, 0.5]							, ):
									snake_case   :					Tuple							=						size if size is not None else {"shortest_edge": 30}
									snake_case   :					Optional[int]							=						parent
									snake_case   :					List[str]							=						batch_size
									snake_case   :					int							=						num_channels
									snake_case   :					int							=						image_size
									snake_case   :					Optional[Any]							=						min_resolution
									snake_case   :					Tuple							=						max_resolution
									snake_case   :					Any							=						do_resize
									snake_case   :					Optional[Any]							=						size
									snake_case   :					List[Any]							=						size_divisor
									snake_case   :					Optional[int]							=						do_normalize
									snake_case   :					List[Any]							=						image_mean
									snake_case   :					List[str]							=						image_std



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

															snake_case   :					Optional[int]							=						int((1_333 / 800) * size				)
															if max(snake_case_							, snake_case_				) > max_size:
																					snake_case   :					Any							=						max_size / max(snake_case_							, snake_case_				)
																					snake_case   :					int							=						newh * scale
																					snake_case   :					List[str]							=						neww * scale

															snake_case							,				snake_case   :					List[str]							=						int(newh + 0.5				), int(neww + 0.5				)
															snake_case							,				snake_case   :					Optional[int]							=						(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

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

									return expected_height, expected_width







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase       ,							unittest.TestCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					ViltImageProcessor if is_vision_available() else None



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[int]							=						ViltImageProcessingTester(self				)



			@property
			def      lowerCamelCase   (       self				):
									return self.image_processor_tester.prepare_image_processor_dict()



			def      lowerCamelCase   (       self				):
									snake_case   :					Dict							=						self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(snake_case_							, "image_mean"				)				)
									self.assertTrue(hasattr(snake_case_							, "image_std"				)				)
									self.assertTrue(hasattr(snake_case_							, "do_normalize"				)				)
									self.assertTrue(hasattr(snake_case_							, "do_resize"				)				)
									self.assertTrue(hasattr(snake_case_							, "size"				)				)
									self.assertTrue(hasattr(snake_case_							, "size_divisor"				)				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[int]							=						self.image_processing_class.from_dict(self.image_processor_dict				)
									self.assertEqual(image_processor.size							, {"shortest_edge": 30}				)

									snake_case   :					Union[str, Any]							=						self.image_processing_class.from_dict(self.image_processor_dict							, size=42				)
									self.assertEqual(image_processor.size							, {"shortest_edge": 42}				)



			def      lowerCamelCase   (       self				):
									pass



			def      lowerCamelCase   (       self				):
									# Initialize image_processing
									snake_case   :					Optional[int]							=						self.image_processing_class(**self.image_processor_dict				)
									# create random PIL images
									snake_case   :					Tuple							=						prepare_image_inputs(self.image_processor_tester							, equal_resolution=snake_case_				)
									for image in image_inputs:
															self.assertIsInstance(snake_case_							, Image.Image				)

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

									snake_case							,				snake_case   :					Optional[Any]							=						self.image_processor_tester.get_expected_values(snake_case_				)
									self.assertEqual(
									    encoded_images.shape							, (1, self.image_processor_tester.num_channels, expected_height, expected_width)							, )

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

									snake_case							,				snake_case   :					Any							=						self.image_processor_tester.get_expected_values(snake_case_							, batched=snake_case_				)
									self.assertEqual(
									    encoded_images.shape							, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							, )



			def      lowerCamelCase   (       self				):
									# Initialize image_processing
									snake_case   :					List[str]							=						self.image_processing_class(**self.image_processor_dict				)
									# create random numpy tensors
									snake_case   :					Tuple							=						prepare_image_inputs(self.image_processor_tester							, equal_resolution=snake_case_							, numpify=snake_case_				)
									for image in image_inputs:
															self.assertIsInstance(snake_case_							, np.ndarray				)

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

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

									# Test batched
									snake_case   :					List[str]							=						image_processing(snake_case_							, return_tensors="pt"				).pixel_values

									snake_case							,				snake_case   :					Any							=						self.image_processor_tester.get_expected_values(snake_case_							, batched=snake_case_				)
									self.assertEqual(
									    encoded_images.shape							, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							, )






			def      lowerCamelCase   (       self				):
									# Initialize image_processing
									snake_case   :					Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict				)
									# create random PyTorch tensors
									snake_case   :					List[str]							=						prepare_image_inputs(self.image_processor_tester							, equal_resolution=snake_case_							, torchify=snake_case_				)
									for image in image_inputs:
															self.assertIsInstance(snake_case_							, torch.Tensor				)

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

									snake_case							,				snake_case   :					Any							=						self.image_processor_tester.get_expected_values(snake_case_				)
									self.assertEqual(
									    encoded_images.shape							, (1, self.image_processor_tester.num_channels, expected_height, expected_width)							, )

									# Test batched
									snake_case   :					Any							=						image_processing(snake_case_							, return_tensors="pt"				).pixel_values

									snake_case							,				snake_case   :					int							=						self.image_processor_tester.get_expected_values(snake_case_							, batched=snake_case_				)
									self.assertEqual(
									    encoded_images.shape							, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							, )






'''simple docstring'''

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


A_        =   {
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "Constraint",
						    "ConstraintListState",
						    "DisjunctiveConstraint",
						    "PhrasalConstraint",
						]
						A_        =   [
						    "BeamHypotheses",
						    "BeamScorer",
						    "BeamSearchScorer",
						    "ConstrainedBeamSearchScorer",
						]
						A_        =   [
						    "EpsilonLogitsWarper",
						    "EtaLogitsWarper",
						    "ForcedBOSTokenLogitsProcessor",
						    "ForcedEOSTokenLogitsProcessor",
						    "HammingDiversityLogitsProcessor",
						    "InfNanRemoveLogitsProcessor",
						    "LogitsProcessor",
						    "LogitsProcessorList",
						    "LogitsWarper",
						    "MinLengthLogitsProcessor",
						    "MinNewTokensLengthLogitsProcessor",
						    "NoBadWordsLogitsProcessor",
						    "NoRepeatNGramLogitsProcessor",
						    "PrefixConstrainedLogitsProcessor",
						    "RepetitionPenaltyLogitsProcessor",
						    "SequenceBiasLogitsProcessor",
						    "EncoderRepetitionPenaltyLogitsProcessor",
						    "TemperatureLogitsWarper",
						    "TopKLogitsWarper",
						    "TopPLogitsWarper",
						    "TypicalLogitsWarper",
						    "EncoderNoRepeatNGramLogitsProcessor",
						    "ExponentialDecayLengthPenalty",
						    "LogitNormalization",
						]
						A_        =   [
						    "MaxNewTokensCriteria",
						    "MaxLengthCriteria",
						    "MaxTimeCriteria",
						    "StoppingCriteria",
						    "StoppingCriteriaList",
						    "validate_stopping_criteria",
						]
						A_        =   [
						    "GenerationMixin",
						    "top_k_top_p_filtering",
						    "GreedySearchEncoderDecoderOutput",
						    "GreedySearchDecoderOnlyOutput",
						    "SampleEncoderDecoderOutput",
						    "SampleDecoderOnlyOutput",
						    "BeamSearchEncoderDecoderOutput",
						    "BeamSearchDecoderOnlyOutput",
						    "BeamSampleEncoderDecoderOutput",
						    "BeamSampleDecoderOnlyOutput",
						    "ContrastiveSearchEncoderDecoderOutput",
						    "ContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "TFForcedBOSTokenLogitsProcessor",
						    "TFForcedEOSTokenLogitsProcessor",
						    "TFLogitsProcessor",
						    "TFLogitsProcessorList",
						    "TFLogitsWarper",
						    "TFMinLengthLogitsProcessor",
						    "TFNoBadWordsLogitsProcessor",
						    "TFNoRepeatNGramLogitsProcessor",
						    "TFRepetitionPenaltyLogitsProcessor",
						    "TFTemperatureLogitsWarper",
						    "TFTopKLogitsWarper",
						    "TFTopPLogitsWarper",
						    "TFForceTokensLogitsProcessor",
						    "TFSuppressTokensAtBeginLogitsProcessor",
						    "TFSuppressTokensLogitsProcessor",
						]
						A_        =   [
						    "TFGenerationMixin",
						    "tf_top_k_top_p_filtering",
						    "TFGreedySearchDecoderOnlyOutput",
						    "TFGreedySearchEncoderDecoderOutput",
						    "TFSampleEncoderDecoderOutput",
						    "TFSampleDecoderOnlyOutput",
						    "TFBeamSearchEncoderDecoderOutput",
						    "TFBeamSearchDecoderOnlyOutput",
						    "TFBeamSampleEncoderDecoderOutput",
						    "TFBeamSampleDecoderOnlyOutput",
						    "TFContrastiveSearchEncoderDecoderOutput",
						    "TFContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "FlaxForcedBOSTokenLogitsProcessor",
						    "FlaxForcedEOSTokenLogitsProcessor",
						    "FlaxLogitsProcessor",
						    "FlaxLogitsProcessorList",
						    "FlaxLogitsWarper",
						    "FlaxMinLengthLogitsProcessor",
						    "FlaxTemperatureLogitsWarper",
						    "FlaxTopKLogitsWarper",
						    "FlaxTopPLogitsWarper",
						]
						A_        =   [
						    "FlaxGenerationMixin",
						    "FlaxGreedySearchOutput",
						    "FlaxSampleOutput",
						    "FlaxBeamSearchOutput",
						]

if TYPE_CHECKING:
						from .configuration_utils import GenerationConfig
						from .streamers import TextIteratorStreamer, TextStreamer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
												from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
												from .logits_process import (
												    EncoderNoRepeatNGramLogitsProcessor,
												    EncoderRepetitionPenaltyLogitsProcessor,
												    EpsilonLogitsWarper,
												    EtaLogitsWarper,
												    ExponentialDecayLengthPenalty,
												    ForcedBOSTokenLogitsProcessor,
												    ForcedEOSTokenLogitsProcessor,
												    HammingDiversityLogitsProcessor,
												    InfNanRemoveLogitsProcessor,
												    LogitNormalization,
												    LogitsProcessor,
												    LogitsProcessorList,
												    LogitsWarper,
												    MinLengthLogitsProcessor,
												    MinNewTokensLengthLogitsProcessor,
												    NoBadWordsLogitsProcessor,
												    NoRepeatNGramLogitsProcessor,
												    PrefixConstrainedLogitsProcessor,
												    RepetitionPenaltyLogitsProcessor,
												    SequenceBiasLogitsProcessor,
												    TemperatureLogitsWarper,
												    TopKLogitsWarper,
												    TopPLogitsWarper,
												    TypicalLogitsWarper,
												)
												from .stopping_criteria import (
												    MaxLengthCriteria,
												    MaxNewTokensCriteria,
												    MaxTimeCriteria,
												    StoppingCriteria,
												    StoppingCriteriaList,
												    validate_stopping_criteria,
												)
												from .utils import (
												    BeamSampleDecoderOnlyOutput,
												    BeamSampleEncoderDecoderOutput,
												    BeamSearchDecoderOnlyOutput,
												    BeamSearchEncoderDecoderOutput,
												    ContrastiveSearchDecoderOnlyOutput,
												    ContrastiveSearchEncoderDecoderOutput,
												    GenerationMixin,
												    GreedySearchDecoderOnlyOutput,
												    GreedySearchEncoderDecoderOutput,
												    SampleDecoderOnlyOutput,
												    SampleEncoderDecoderOutput,
												    top_k_top_p_filtering,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tf_logits_process import (
												    TFForcedBOSTokenLogitsProcessor,
												    TFForcedEOSTokenLogitsProcessor,
												    TFForceTokensLogitsProcessor,
												    TFLogitsProcessor,
												    TFLogitsProcessorList,
												    TFLogitsWarper,
												    TFMinLengthLogitsProcessor,
												    TFNoBadWordsLogitsProcessor,
												    TFNoRepeatNGramLogitsProcessor,
												    TFRepetitionPenaltyLogitsProcessor,
												    TFSuppressTokensAtBeginLogitsProcessor,
												    TFSuppressTokensLogitsProcessor,
												    TFTemperatureLogitsWarper,
												    TFTopKLogitsWarper,
												    TFTopPLogitsWarper,
												)
												from .tf_utils import (
												    TFBeamSampleDecoderOnlyOutput,
												    TFBeamSampleEncoderDecoderOutput,
												    TFBeamSearchDecoderOnlyOutput,
												    TFBeamSearchEncoderDecoderOutput,
												    TFContrastiveSearchDecoderOnlyOutput,
												    TFContrastiveSearchEncoderDecoderOutput,
												    TFGenerationMixin,
												    TFGreedySearchDecoderOnlyOutput,
												    TFGreedySearchEncoderDecoderOutput,
												    TFSampleDecoderOnlyOutput,
												    TFSampleEncoderDecoderOutput,
												    tf_top_k_top_p_filtering,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .flax_logits_process import (
												    FlaxForcedBOSTokenLogitsProcessor,
												    FlaxForcedEOSTokenLogitsProcessor,
												    FlaxLogitsProcessor,
												    FlaxLogitsProcessorList,
												    FlaxLogitsWarper,
												    FlaxMinLengthLogitsProcessor,
												    FlaxTemperatureLogitsWarper,
												    FlaxTopKLogitsWarper,
												    FlaxTopPLogitsWarper,
												)
												from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
						import sys

						A_        =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





