





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Dict=False  ,				__lowercase:		Optional[Any]=False  ,				__lowercase:		int=False):



  '''simple docstring'''






  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  __lowerCAmelCase								=					[]
  for i in range(image_processor_tester.batch_size):
    if equal_resolution:
      __lowerCAmelCase								=					image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      __lowerCAmelCase								=					image_processor_tester.min_resolution
      if getattr(__lowerCAmelCase  ,				'''size_divisor'''  ,				__lowerCAmelCase):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        __lowerCAmelCase								=					max(image_processor_tester.size_divisor  ,				__lowerCAmelCase)
      __lowerCAmelCase								=					np.random.choice(np.arange(__lowerCAmelCase  ,				image_processor_tester.max_resolution)  ,				2)
    image_inputs.append(
        np.random.randint(2_55  ,				size=(image_processor_tester.num_channels, width, height)  ,				dtype=np.uinta))

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    __lowerCAmelCase								=					[Image.fromarray(np.moveaxis(__lowerCAmelCase  ,				0  ,				-1)) for image in image_inputs]

  if torchify:
    __lowerCAmelCase								=					[torch.from_numpy(__lowerCAmelCase) for image in image_inputs]

  return image_inputs


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]  ,				__lowercase:		Tuple=10  ,				__lowercase:		str=10  ,				__lowercase:		Any=False  ,				__lowercase:		List[Any]=False):



  '''simple docstring'''






  __lowerCAmelCase								=					[]
  for i in range(image_processor_tester.num_frames):
    video.append(np.random.randint(2_55  ,				size=(image_processor_tester.num_channels, width, height)  ,				dtype=np.uinta))

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    __lowerCAmelCase								=					[Image.fromarray(np.moveaxis(__lowerCAmelCase  ,				0  ,				-1)) for frame in video]

  if torchify:
    __lowerCAmelCase								=					[torch.from_numpy(__lowerCAmelCase) for frame in video]

  return video


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]=False  ,				__lowercase:		Any=False  ,				__lowercase:		Dict=False):



  '''simple docstring'''






  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  __lowerCAmelCase								=					[]
  for i in range(image_processor_tester.batch_size):
    if equal_resolution:
      __lowerCAmelCase								=					image_processor_tester.max_resolution
    else:
      __lowerCAmelCase								=					np.random.choice(
          np.arange(image_processor_tester.min_resolution  ,				image_processor_tester.max_resolution)  ,				2)
      __lowerCAmelCase								=					prepare_video(
          image_processor_tester=__lowerCAmelCase  ,				width=__lowerCAmelCase  ,				height=__lowerCAmelCase  ,				numpify=__lowerCAmelCase  ,				torchify=__lowerCAmelCase  ,				)
    video_inputs.append(__lowerCAmelCase)

  return video_inputs




class 	__snake_case					:






    '''simple docstring'''
    _lowerCAmelCase  =						None





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.image_processing_class(**self.image_processor_dict				)
      __lowerCAmelCase								=					json.loads(image_processor.to_json_string()				)
      for key, value in self.image_processor_dict.items():
        self.assertEqual(obj[key]      ,    lowerCAmelCase__				)





    def 						A		( self    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					self.image_processing_class(**self.image_processor_dict				)

      with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=					os.path.join(lowerCAmelCase__      ,    '''image_processor.json'''				)
        image_processor_first.to_json_file(lowerCAmelCase__				)
        __lowerCAmelCase								=					self.image_processing_class.from_json_file(lowerCAmelCase__				)

      self.assertEqual(image_processor_second.to_dict()      ,    image_processor_first.to_dict()				)





    def 						A		( self    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					self.image_processing_class(**self.image_processor_dict				)

      with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=					image_processor_first.save_pretrained(lowerCAmelCase__				)[0]
        check_json_file_has_correct_format(lowerCAmelCase__				)
        __lowerCAmelCase								=					self.image_processing_class.from_pretrained(lowerCAmelCase__				)

      self.assertEqual(image_processor_second.to_dict()      ,    image_processor_first.to_dict()				)





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.image_processing_class()
      self.assertIsNotNone(lowerCAmelCase__				)





    @require_torch
    @require_vision
    def 						A		( self    :		Union[str, Any]				):

      """simple docstring"""





      if self.test_cast_dtype is not None:
        # Initialize image_processor
        __lowerCAmelCase								=					self.image_processing_class(**self.image_processor_dict				)

        # create random PyTorch tensors
        __lowerCAmelCase								=					prepare_image_inputs(self.image_processor_tester      ,    equal_resolution=lowerCAmelCase__      ,    torchify=lowerCAmelCase__				)

        __lowerCAmelCase								=					image_processor(lowerCAmelCase__      ,    return_tensors='''pt'''				)
        # for layoutLM compatiblity
        self.assertEqual(encoding.pixel_values.device      ,    torch.device('''cpu'''				)				)
        self.assertEqual(encoding.pixel_values.dtype      ,    torch.floataa				)

        __lowerCAmelCase								=					image_processor(lowerCAmelCase__      ,    return_tensors='''pt'''				).to(torch.floataa				)
        self.assertEqual(encoding.pixel_values.device      ,    torch.device('''cpu'''				)				)
        self.assertEqual(encoding.pixel_values.dtype      ,    torch.floataa				)

        __lowerCAmelCase								=					image_processor(lowerCAmelCase__      ,    return_tensors='''pt'''				).to('''cpu'''      ,    torch.bfloataa				)
        self.assertEqual(encoding.pixel_values.device      ,    torch.device('''cpu'''				)				)
        self.assertEqual(encoding.pixel_values.dtype      ,    torch.bfloataa				)

        with self.assertRaises(lowerCAmelCase__				):
          __lowerCAmelCase								=					image_processor(lowerCAmelCase__      ,    return_tensors='''pt'''				).to(torch.bfloataa      ,    '''cpu'''				)

        # Try with text + image feature
        __lowerCAmelCase								=					image_processor(lowerCAmelCase__      ,    return_tensors='''pt'''				)
        encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]				)}				)
        __lowerCAmelCase								=					encoding.to(torch.floataa				)

        self.assertEqual(encoding.pixel_values.device      ,    torch.device('''cpu'''				)				)
        self.assertEqual(encoding.pixel_values.dtype      ,    torch.floataa				)
        self.assertEqual(encoding.input_ids.dtype      ,    torch.long				)






"""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,
)


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

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

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_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

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



