



'''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 _A	(     unittest.TestCase     ):





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=7			,							__UpperCAmelCase=3			,							__UpperCAmelCase=18			,							__UpperCAmelCase=30			,							__UpperCAmelCase=400			,							__UpperCAmelCase=True			,							__UpperCAmelCase=None			,							__UpperCAmelCase=2			,							__UpperCAmelCase=True			,							__UpperCAmelCase=[0.5, 0.5, 0.5]			,							__UpperCAmelCase=[0.5, 0.5, 0.5]			,							)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  size if size is not None else {"""shortest_edge""": 30}
					__UpperCAmelCase :   Optional[Any]    					=  parent
					__UpperCAmelCase :   int    					=  batch_size
					__UpperCAmelCase :   List[str]    					=  num_channels
					__UpperCAmelCase :   str    					=  image_size
					__UpperCAmelCase :   Any    					=  min_resolution
					__UpperCAmelCase :   Optional[Any]    					=  max_resolution
					__UpperCAmelCase :   List[Any]    					=  do_resize
					__UpperCAmelCase :   Any    					=  size
					__UpperCAmelCase :   int    					=  size_divisor
					__UpperCAmelCase :   Tuple    					=  do_normalize
					__UpperCAmelCase :   Union[str, Any]    					=  image_mean
					__UpperCAmelCase :   Optional[int]    					=  image_std





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

					'''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 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=False					)							->  List[Any]:

					'''simple docstring'''
					if not batched:
								__UpperCAmelCase :   Any    					=  self.size["""shortest_edge"""]
								__UpperCAmelCase :   str    					=  image_inputs[0]
								if isinstance(__UpperCAmelCase			,							Image.Image					):
											__UpperCAmelCase					,	__UpperCAmelCase :   Optional[int]    					=  image.size
								else:
											__UpperCAmelCase					,	__UpperCAmelCase :   Optional[Any]    					=  image.shape[1], image.shape[2]
								__UpperCAmelCase :   str    					=  size / min(__UpperCAmelCase			,							__UpperCAmelCase					)
								if h < w:
											__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  size, scale * w
								else:
											__UpperCAmelCase					,	__UpperCAmelCase :   List[Any]    					=  scale * h, size

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

								__UpperCAmelCase					,	__UpperCAmelCase :   Tuple    					=  int(newh + 0.5					), int(neww + 0.5					)
								__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

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

					return expected_height, expected_width



@require_torch
@require_vision
class _A	(     __SCREAMING_SNAKE_CASE						,					unittest.TestCase     ):
		_SCREAMING_SNAKE_CASE       :     List[str]       =      ViltImageProcessor if is_vision_available() else None





		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  ViltImageProcessingTester(self					)





		@property
		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""image_mean"""					)					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""image_std"""					)					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""do_normalize"""					)					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""do_resize"""					)					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""size"""					)					)
					self.assertTrue(hasattr(__UpperCAmelCase			,							"""size_divisor"""					)					)





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  self.image_processing_class.from_dict(self.image_processor_dict					)
					self.assertEqual(image_processor.size			,							{"""shortest_edge""": 30}					)

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





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					pass





		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase :   Optional[int]    					=  self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__UpperCAmelCase :   Union[str, Any]    					=  prepare_image_inputs(self.image_processor_tester			,							equal_resolution=__UpperCAmelCase					)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase			,							Image.Image					)

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

					__UpperCAmelCase					,	__UpperCAmelCase :   Tuple    					=  self.image_processor_tester.get_expected_values(__UpperCAmelCase					)
					self.assertEqual(
					    encoded_images.shape			,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,							)

					# Test batched
					__UpperCAmelCase :   Tuple    					=  image_processing(__UpperCAmelCase			,							return_tensors="""pt"""					).pixel_values

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





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase :   Tuple    					=  self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__UpperCAmelCase :   Optional[int]    					=  prepare_image_inputs(self.image_processor_tester			,							equal_resolution=__UpperCAmelCase			,							numpify=__UpperCAmelCase					)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase			,							np.ndarray					)

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

					__UpperCAmelCase					,	__UpperCAmelCase :   Tuple    					=  self.image_processor_tester.get_expected_values(__UpperCAmelCase					)
					self.assertEqual(
					    encoded_images.shape			,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,							)

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

					__UpperCAmelCase					,	__UpperCAmelCase :   Tuple    					=  self.image_processor_tester.get_expected_values(__UpperCAmelCase			,							batched=__UpperCAmelCase					)
					self.assertEqual(
					    encoded_images.shape			,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,							)





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

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase :   Union[str, Any]    					=  self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__UpperCAmelCase :   Dict    					=  prepare_image_inputs(self.image_processor_tester			,							equal_resolution=__UpperCAmelCase			,							torchify=__UpperCAmelCase					)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase			,							torch.Tensor					)

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

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

					# Test batched
					__UpperCAmelCase :   Optional[int]    					=  image_processing(__UpperCAmelCase			,							return_tensors="""pt"""					).pixel_values

					__UpperCAmelCase					,	__UpperCAmelCase :   List[Any]    					=  self.image_processor_tester.get_expected_values(__UpperCAmelCase			,							batched=__UpperCAmelCase					)
					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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




