
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 MobileNetVaImageProcessor







class 							UpperCAmelCase	(   unittest.TestCase					):

					def __init__(			self      :					Any ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Dict=7 ,      lowerCAmelCase      :					List[str]=3 ,      lowerCAmelCase      :					List[Any]=18 ,      lowerCAmelCase      :					Dict=30 ,      lowerCAmelCase      :					Optional[Any]=400 ,      lowerCAmelCase      :					Tuple=True ,      lowerCAmelCase      :					Union[str, Any]=None ,      lowerCAmelCase      :					List[Any]=True ,      lowerCAmelCase      :					Any=None ,      ):
											lowercase       :    Union[str, Any]											=  size if size is not None else {'''shortest_edge''': 20}
											lowercase       :    Dict											=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
											lowercase       :    Any											=  parent
											lowercase       :    List[Any]											=  batch_size
											lowercase       :    Union[str, Any]											=  num_channels
											lowercase       :    Optional[int]											=  image_size
											lowercase       :    Any											=  min_resolution
											lowercase       :    List[str]											=  max_resolution
											lowercase       :    str											=  do_resize
											lowercase       :    int											=  size
											lowercase       :    Optional[Any]											=  do_center_crop
											lowercase       :    Dict											=  crop_size




					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}







@require_torch
@require_vision
class 							UpperCAmelCase	(   __lowerCamelCase						, unittest.TestCase					):
					a__: str			  =				MobileNetVaImageProcessor if is_vision_available() else None

					def        _lowerCAmelCase	(			self      :					str					):
											lowercase       :    List[str]											=  MobileNetVaImageProcessingTester(self					)

					@property
					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											return self.image_processor_tester.prepare_image_processor_dict()

					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    Optional[Any]											=  self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''do_resize'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''size'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''do_center_crop'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''center_crop'''					)					)

					def        _lowerCAmelCase	(			self      :					str					):
											lowercase       :    Dict											=  self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size ,      {'''shortest_edge''': 20}					)
											self.assertEqual(image_processor.crop_size ,      {'''height''': 18, '''width''': 18}					)

											lowercase       :    Dict											=  self.image_processing_class.from_dict(self.image_processor_dict ,      size=42 ,      crop_size=84					)
											self.assertEqual(image_processor.size ,      {'''shortest_edge''': 42}					)
											self.assertEqual(image_processor.crop_size ,      {'''height''': 84, '''width''': 84}					)

					def        _lowerCAmelCase	(			self      :					Any					):
											pass

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											# Initialize image_processing
											lowercase       :    Dict											=  self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											lowercase       :    Tuple											=  prepare_image_inputs(self.image_processor_tester ,      equal_resolution=lowerCAmelCase					)
											for image in image_inputs:
																	self.assertIsInstance(lowerCAmelCase ,      Image.Image					)

											# Test not batched input
											lowercase       :    List[str]											=  image_processing(image_inputs[0] ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,      )

											# Test batched
											lowercase       :    List[str]											=  image_processing(lowerCAmelCase ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,      )

					def        _lowerCAmelCase	(			self      :					Dict					):
											# Initialize image_processing
											lowercase       :    Dict											=  self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											lowercase       :    Dict											=  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
											lowercase       :    Any											=  image_processing(image_inputs[0] ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,      )

											# Test batched
											lowercase       :    Optional[Any]											=  image_processing(lowerCAmelCase ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,      )




					def        _lowerCAmelCase	(			self      :					Any					):
											# Initialize image_processing
											lowercase       :    Optional[Any]											=  self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											lowercase       :    Dict											=  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
											lowercase       :    Dict											=  image_processing(image_inputs[0] ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    ) ,      )

											# Test batched
											lowercase       :    List[Any]											=  image_processing(lowerCAmelCase ,      return_tensors='''pt'''					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''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,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """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:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """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

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


