


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def __init__(    self:	Any						,				a:	Any						,				a:	Any=7						,				a:	Union[str, Any]=3						,				a:	int=18						,				a:	Dict=30						,				a:	Optional[Any]=400						,				a:	Dict=True						,				a:	Tuple=None						,				a:	Tuple=True						,				a:	Optional[int]=False						,				a:	Union[str, Any]=True						,				a:	Optional[int]=True						,				a:	Union[str, Any]=[0.5, 0.5, 0.5]						,				a:	Union[str, Any]=[0.5, 0.5, 0.5]						,				):
       __lowerCamelCase		:							Union[str, Any]		 =						parent
       __lowerCamelCase		:							Optional[int]		 =						batch_size
       __lowerCamelCase		:							Dict		 =						num_channels
       __lowerCamelCase		:							Tuple		 =						image_size
       __lowerCamelCase		:							Union[str, Any]		 =						min_resolution
       __lowerCamelCase		:							Dict		 =						max_resolution
       __lowerCamelCase		:							List[Any]		 =						do_resize
       __lowerCamelCase		:							List[Any]		 =						size if size is not None else {'height': 18, 'width': 20}
       __lowerCamelCase		:							int		 =						do_thumbnail
       __lowerCamelCase		:							List[Any]		 =						do_align_axis
       __lowerCamelCase		:							Optional[Any]		 =						do_pad
       __lowerCamelCase		:							List[Any]		 =						do_normalize
       __lowerCamelCase		:							Tuple		 =						image_mean
       __lowerCamelCase		:							List[str]		 =						image_std




      def 					_snake_case     (    self:	Any    ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }



@require_torch
@require_vision
class 	A_ (	__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       DonutImageProcessor if is_vision_available() else None




      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Tuple		 =						DonutImageProcessingTester(self    )




      @property
      def 					_snake_case     (    self:	int    ):
       return self.image_processor_tester.prepare_image_processor_dict()




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							str		 =						self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(a						,				'do_resize'    )    )
       self.assertTrue(hasattr(a						,				'size'    )    )
       self.assertTrue(hasattr(a						,				'do_thumbnail'    )    )
       self.assertTrue(hasattr(a						,				'do_align_long_axis'    )    )
       self.assertTrue(hasattr(a						,				'do_pad'    )    )
       self.assertTrue(hasattr(a						,				'do_normalize'    )    )
       self.assertTrue(hasattr(a						,				'image_mean'    )    )
       self.assertTrue(hasattr(a						,				'image_std'    )    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							int		 =						self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}    )

       __lowerCamelCase		:							str		 =						self.image_processing_class.from_dict(self.image_processor_dict						,				size=42    )
       self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}    )

       # Previous config had dimensions in (width, height) order
       __lowerCamelCase		:							List[Any]		 =						self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)    )
       self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}    )




      def 					_snake_case     (    self:	Dict    ):
       pass




      @is_flaky()
      def 					_snake_case     (    self:	Any    ):
       # Initialize image_processing
       __lowerCamelCase		:							str		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       __lowerCamelCase		:							List[Any]		 =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=a    )
       for image in image_inputs:
        self.assertIsInstance(a						,				Image.Image    )

       # Test not batched input
       __lowerCamelCase		:							Optional[int]		 =						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
       __lowerCamelCase		:							Dict		 =						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'],
           )						,				)




      @is_flaky()
      def 					_snake_case     (    self:	str    ):
       # Initialize image_processing
       __lowerCamelCase		:							List[Any]		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       __lowerCamelCase		:							Union[str, Any]		 =						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
       __lowerCamelCase		:							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.size['height'],
               self.image_processor_tester.size['width'],
           )						,				)

       # Test batched
       __lowerCamelCase		:							Union[str, Any]		 =						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'],
           )						,				)




      @is_flaky()
      def 					_snake_case     (    self:	int    ):
       # Initialize image_processing
       __lowerCamelCase		:							List[str]		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       __lowerCamelCase		:							Dict		 =						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
       __lowerCamelCase		:							Optional[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.size['height'],
               self.image_processor_tester.size['width'],
           )						,				)

       # Test batched
       __lowerCamelCase		:							Union[str, Any]		 =						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'],
           )						,				)






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,
)


lowercase_	   =     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

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

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

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

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

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           '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

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

