






"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class  lowerCamelCase__ (	unittest.TestCase			):






    def __init__(			self     ,A     ,A=7     ,A=3     ,A=18     ,A=30     ,A=400     ,A=True     ,A=None     ,A=True     ,A=[0.5, 0.5, 0.5]     ,A=[0.5, 0.5, 0.5]     ,):
          UpperCAmelCase          =	size if size is not None else {"""height""": 18, """width""": 18}
          UpperCAmelCase          =	parent
          UpperCAmelCase          =	batch_size
          UpperCAmelCase          =	num_channels
          UpperCAmelCase          =	image_size
          UpperCAmelCase          =	min_resolution
          UpperCAmelCase          =	max_resolution
          UpperCAmelCase          =	do_resize
          UpperCAmelCase          =	size
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	image_mean
          UpperCAmelCase          =	image_std






    def     _UpperCamelCase							(			self ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class  lowerCamelCase__ (	snake_case   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      DPTImageProcessor if is_vision_available() else None






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	DPTImageProcessingTester(self )






    @property
    def     _UpperCamelCase							(			self ):
          return self.image_processor_tester.prepare_image_processor_dict()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(A     ,"""image_mean""" ) )
          self.assertTrue(hasattr(A     ,"""image_std""" ) )
          self.assertTrue(hasattr(A     ,"""do_normalize""" ) )
          self.assertTrue(hasattr(A     ,"""do_resize""" ) )
          self.assertTrue(hasattr(A     ,"""size""" ) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size     ,{"""height""": 18, """width""": 18} )

          UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict     ,size=42 )
          self.assertEqual(image_processor.size     ,{"""height""": 42, """width""": 42} )






    def     _UpperCamelCase							(			self ):
          # Initialize image_processing
          UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A )
          for image in image_inputs:
                self.assertIsInstance(A     ,Image.Image )

          # Test not batched input
          UpperCAmelCase          =	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,)

          # Test batched
          UpperCAmelCase          =	image_processing(A     ,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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,)






    def     _UpperCamelCase							(			self ):
          # Initialize image_processing
          UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A     ,numpify=A )
          for image in image_inputs:
                self.assertIsInstance(A     ,np.ndarray )

          # Test not batched input
          UpperCAmelCase          =	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,)

          # Test batched
          UpperCAmelCase          =	image_processing(A     ,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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,)






    def     _UpperCamelCase							(			self ):
          # Initialize image_processing
          UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A     ,torchify=A )
          for image in image_inputs:
                self.assertIsInstance(A     ,torch.Tensor )

          # Test not batched input
          UpperCAmelCase          =	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,)

          # Test batched
          UpperCAmelCase          =	image_processing(A     ,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.size["""height"""],
                  self.image_processor_tester.size["""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_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MBartForCausalLM""",
      """MBartForConditionalGeneration""",
      """MBartForQuestionAnswering""",
      """MBartForSequenceClassification""",
      """MBartModel""",
      """MBartPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TFMBartForConditionalGeneration""",
      """TFMBartModel""",
      """TFMBartPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FlaxMBartForConditionalGeneration""",
      """FlaxMBartForQuestionAnswering""",
      """FlaxMBartForSequenceClassification""",
      """FlaxMBartModel""",
      """FlaxMBartPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart import MBartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart_fast import MBartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mbart import (
        MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        MBartForCausalLM,
        MBartForConditionalGeneration,
        MBartForQuestionAnswering,
        MBartForSequenceClassification,
        MBartModel,
        MBartPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        FlaxMBartPreTrainedModel,
    )

else:
  import sys

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



