
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 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case = True		,     _snake_case = None		,     _snake_case = 32		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = True		,     _snake_case = [0.4814_5466, 0.457_8275, 0.4082_1073]		,     _snake_case = [0.2686_2954, 0.2613_0258, 0.2757_7711]		,     _snake_case = True		,     _snake_case=7		,     _snake_case=30		,     _snake_case=400		,     _snake_case=3		,     )    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    parent
								__a										=    do_resize
								__a										=    size if size is not None else {'''shortest_edge''': 288}
								__a										=    size_divisor
								__a										=    do_rescale
								__a										=    rescale_factor
								__a										=    do_normalize
								__a										=    do_center_crop
								__a										=    image_mean
								__a										=    image_std
								__a										=    do_pad
								__a										=    batch_size
								__a										=    num_channels
								__a										=    min_resolution
								__a										=    max_resolution




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      str:


								'''simple docstring'''




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

												__a										=    int((1_333 / 800) * size				)
												if max(_snake_case		,     _snake_case				) > max_size:
																__a										=    max_size / max(_snake_case		,     _snake_case				)
																__a										=    newh * scale
																__a										=    neww * scale

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

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

								return expected_height, expected_width







@require_torch
@require_vision
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    BridgeTowerImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    BridgeTowerImageProcessingTester(self				)




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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

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

								__a							,     __a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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

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

								__a							,     __a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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

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

								__a							,     __a										=    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__)