



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    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		List[str]=False		,	lowerCAmelCase__:		int=False		,	lowerCAmelCase__:		Optional[int]=False     ):





   """simple docstring"""


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

   UpperCAmelCase_:		Dict       			=    []
   for i in range(image_processor_tester.batch_size     ):
      if equal_resolution:
         UpperCAmelCase_:		List[Any]       			=    image_processor_tester.max_resolution
      else:
         # To avoid getting image width/height 0
         UpperCAmelCase_:		str       			=    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`
            UpperCAmelCase_:		Union[str, Any]       			=    max(image_processor_tester.size_divisor		,	lowerCAmelCase__     )
         UpperCAmelCase_:		Any       			=    np.random.choice(np.arange(lowerCAmelCase__		,	image_processor_tester.max_resolution     )		,	2     )
      image_inputs.append(
          np.random.randint(2_5_5		,	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
      UpperCAmelCase_:		List[Any]       			=    [Image.fromarray(np.moveaxis(lowerCAmelCase__		,	0		,	-1     )     ) for image in image_inputs]

   if torchify:
      UpperCAmelCase_:		Union[str, Any]       			=    [torch.from_numpy(lowerCAmelCase__     ) for image in image_inputs]

   return image_inputs







def    lowerCAmelCase_  (lowerCAmelCase__:		str		,	lowerCAmelCase__:		Any=1_0		,	lowerCAmelCase__:		Tuple=1_0		,	lowerCAmelCase__:		Optional[Any]=False		,	lowerCAmelCase__:		Union[str, Any]=False     ):





   """simple docstring"""


   UpperCAmelCase_:		Union[str, Any]       			=    []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(2_5_5		,	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
      UpperCAmelCase_:		int       			=    [Image.fromarray(np.moveaxis(lowerCAmelCase__		,	0		,	-1     )     ) for frame in video]

   if torchify:
      UpperCAmelCase_:		int       			=    [torch.from_numpy(lowerCAmelCase__     ) for frame in video]

   return video







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]		,	lowerCAmelCase__:		Dict=False		,	lowerCAmelCase__:		Dict=False		,	lowerCAmelCase__:		Optional[int]=False     ):





   """simple docstring"""


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

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

   return video_inputs







class 							_a							:
   A					=  None

   def     __snake_case					(self				)					->      Any:
      UpperCAmelCase_:		Tuple       			=    self.image_processing_class(**self.image_processor_dict				)
      UpperCAmelCase_:		str       			=    json.loads(image_processor.to_json_string()				)
      for key, value in self.image_processor_dict.items():
         self.assertEqual(obj[key],      SCREAMING_SNAKE_CASE_				)

   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)

      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCAmelCase_:		str       			=    os.path.join(SCREAMING_SNAKE_CASE_,      """image_processor.json"""				)
         image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_				)
         UpperCAmelCase_:		str       			=    self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_				)

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

   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		Optional[Any]       			=    self.image_processing_class(**self.image_processor_dict				)

      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCAmelCase_:		Dict       			=    image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_				)[0]
         check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_				)
         UpperCAmelCase_:		List[str]       			=    self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_				)

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

   def     __snake_case					(self				)					->      Tuple:
      UpperCAmelCase_:		int       			=    self.image_processing_class()
      self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)

   @require_torch
   @require_vision
   def     __snake_case					(self				)					->      List[Any]:
      if self.test_cast_dtype is not None:
         # Initialize image_processor
         UpperCAmelCase_:		Tuple       			=    self.image_processing_class(**self.image_processor_dict				)

         # create random PyTorch tensors
         UpperCAmelCase_:		List[Any]       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_				)

         UpperCAmelCase_:		Dict       			=    image_processor(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				)
         # for layoutLM compatiblity
         self.assertEqual(encoding.pixel_values.device,      torch.device("""cpu"""				)				)
         self.assertEqual(encoding.pixel_values.dtype,      torch.floataa				)

         UpperCAmelCase_:		Union[str, Any]       			=    image_processor(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).to(torch.floataa				)
         self.assertEqual(encoding.pixel_values.device,      torch.device("""cpu"""				)				)
         self.assertEqual(encoding.pixel_values.dtype,      torch.floataa				)

         UpperCAmelCase_:		Tuple       			=    image_processor(SCREAMING_SNAKE_CASE_,      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(SCREAMING_SNAKE_CASE_				):
            UpperCAmelCase_:		Any       			=    image_processor(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).to(torch.bfloataa,      """cpu"""				)

         # Try with text + image feature
         UpperCAmelCase_:		Dict       			=    image_processor(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				)
         encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]				)}				)
         UpperCAmelCase_:		Tuple       			=    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				)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






