



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						):



			def __init__(						self   :  Any ,		A   :  int ,		A   :  Any = True ,		A   :  int = None ,		A   :  Dict = 32 ,		A   :  Optional[Any] = True ,		A   :  Tuple = 1 / 2_55 ,		A   :  str = True ,		A   :  List[Any] = True ,		A   :  List[Any] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,		A   :  Tuple = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,		A   :  Union[str, Any] = True ,		A   :  int=7 ,		A   :  int=30 ,		A   :  Union[str, Any]=4_00 ,		A   :  Tuple=3 ,		)   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   parent
						_UpperCAmelCase		       =   do_resize
						_UpperCAmelCase		       =   size if size is not None else {'shortest_edge': 2_88}
						_UpperCAmelCase		       =   size_divisor
						_UpperCAmelCase		       =   do_rescale
						_UpperCAmelCase		       =   rescale_factor
						_UpperCAmelCase		       =   do_normalize
						_UpperCAmelCase		       =   do_center_crop
						_UpperCAmelCase		       =   image_mean
						_UpperCAmelCase		       =   image_std
						_UpperCAmelCase		       =   do_pad
						_UpperCAmelCase		       =   batch_size
						_UpperCAmelCase		       =   num_channels
						_UpperCAmelCase		       =   min_resolution
						_UpperCAmelCase		       =   max_resolution



			def     _lowerCamelCase       (						self   :  List[str])   ->    Any:



						"""simple docstring"""






						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   :  Any ,		A   :  Any ,		A   :  str=False)   ->    str:



						"""simple docstring"""






						if not batched:
									_UpperCAmelCase		       =   self.size['shortest_edge']
									_UpperCAmelCase		       =   image_inputs[0]
									if isinstance(_snake_case ,		Image.Image):
												_UpperCAmelCase       ,     _UpperCAmelCase		       =   image.size
									else:
												_UpperCAmelCase       ,     _UpperCAmelCase		       =   image.shape[1], image.shape[2]
									_UpperCAmelCase		       =   size / min(_snake_case ,		_snake_case)
									if h < w:
												_UpperCAmelCase       ,     _UpperCAmelCase		       =   size, scale * w
									else:
												_UpperCAmelCase       ,     _UpperCAmelCase		       =   scale * h, size

									_UpperCAmelCase		       =   int((13_33 / 8_00) * size)
									if max(_snake_case ,		_snake_case) > max_size:
												_UpperCAmelCase		       =   max_size / max(_snake_case ,		_snake_case)
												_UpperCAmelCase		       =   newh * scale
												_UpperCAmelCase		       =   neww * scale

									_UpperCAmelCase       ,     _UpperCAmelCase		       =   int(newh + 0.5), int(neww + 0.5)
									_UpperCAmelCase       ,     _UpperCAmelCase		       =   (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									_UpperCAmelCase		       =   []
									for image in image_inputs:
												_UpperCAmelCase       ,     _UpperCAmelCase		       =   self.get_expected_values([image])
												expected_values.append((expected_height, expected_width))
									_UpperCAmelCase		       =   max(_snake_case ,		key=lambda A: item[0])[0]
									_UpperCAmelCase		       =   max(_snake_case ,		key=lambda A: item[1])[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 			__lowerCAmelCase    (			A				,						unittest.TestCase						):
			UpperCamelCase   					=     BridgeTowerImageProcessor if is_vision_available() else None



			def     _lowerCamelCase       (						self   :  Tuple)   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   BridgeTowerImageProcessingTester(self)



			@property
			def     _lowerCamelCase       (						self   :  Dict)   ->    List[str]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def     _lowerCamelCase       (						self   :  List[str])   ->    List[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   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   :  List[Any])   ->    str:



						"""simple docstring"""






						pass



			def     _lowerCamelCase       (						self   :  Tuple)   ->    List[str]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(image_inputs[0] ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(_snake_case ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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   :  Tuple)   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(image_inputs[0] ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(_snake_case ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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   :  List[str])   ->    int:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(image_inputs[0] ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   image_processing(_snake_case ,		return_tensors='pt').pixel_values

						_UpperCAmelCase       ,     _UpperCAmelCase		       =   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,
						    ) ,		)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)