






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_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    List[str]=False      ,     __magic_name__   :    Union[str, Any]=False      ,     __magic_name__   :    Any=False   )						->     Optional[int]:






	"""simple docstring"""



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

	UpperCamelCase	:int		=      []
	for i in range(image_processor_tester.batch_size   ):
		if equal_resolution:
			UpperCamelCase	:Optional[Any]		=      image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			UpperCamelCase	:List[Any]		=      image_processor_tester.min_resolution
			if getattr(__magic_name__      ,     """size_divisor"""      ,     __magic_name__   ):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				UpperCamelCase	:int		=      max(image_processor_tester.size_divisor      ,     __magic_name__   )
			UpperCamelCase	, UpperCamelCase	:List[str]		=      np.random.choice(np.arange(__magic_name__      ,     image_processor_tester.max_resolution   )      ,     2   )
		image_inputs.append(
		    np.random.randint(255      ,     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	:Any		=      [Image.fromarray(np.moveaxis(__magic_name__      ,     0      ,     -1   )   ) for image in image_inputs]

	if torchify:
		UpperCamelCase	:Dict		=      [torch.from_numpy(__magic_name__   ) for image in image_inputs]

	return image_inputs

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Any      ,     __magic_name__   :    int=10      ,     __magic_name__   :    Dict=10      ,     __magic_name__   :    List[Any]=False      ,     __magic_name__   :    Optional[int]=False   )						->     List[Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      []
	for i in range(image_processor_tester.num_frames   ):
		video.append(np.random.randint(255      ,     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	:Union[str, Any]		=      [Image.fromarray(np.moveaxis(__magic_name__      ,     0      ,     -1   )   ) for frame in video]

	if torchify:
		UpperCamelCase	:List[Any]		=      [torch.from_numpy(__magic_name__   ) for frame in video]

	return video

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Union[str, Any]=False      ,     __magic_name__   :    Union[str, Any]=False      ,     __magic_name__   :    Dict=False   )						->     List[Any]:






	"""simple docstring"""



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

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

	return video_inputs

class        _SCREAMING_SNAKE_CASE			:
	snake_case__				:	Union[str, Any]	      =    None





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:List[str]		=      self.image_processing_class(**self.image_processor_dict      )
		UpperCamelCase	:List[Any]		=      json.loads(image_processor.to_json_string()      )
		for key, value in self.image_processor_dict.items():
			self.assertEqual(obj[key]					,    __lowerCamelCase      )





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Any		=      self.image_processing_class(**self.image_processor_dict      )

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCamelCase	:str		=      os.path.join(__lowerCamelCase					,    """image_processor.json"""      )
			image_processor_first.to_json_file(__lowerCamelCase      )
			UpperCamelCase	:Optional[Any]		=      self.image_processing_class.from_json_file(__lowerCamelCase      )

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





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:Any		=      self.image_processing_class(**self.image_processor_dict      )

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCamelCase	:str		=      image_processor_first.save_pretrained(__lowerCamelCase      )[0]
			check_json_file_has_correct_format(__lowerCamelCase      )
			UpperCamelCase	:Union[str, Any]		=      self.image_processing_class.from_pretrained(__lowerCamelCase      )

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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      self.image_processing_class()
		self.assertIsNotNone(__lowerCamelCase      )





	@require_torch
	@require_vision
	def 		_A					(						self							:   str      ):
		if self.test_cast_dtype is not None:
			# Initialize image_processor
			UpperCamelCase	:List[Any]		=      self.image_processing_class(**self.image_processor_dict      )

			# create random PyTorch tensors
			UpperCamelCase	:Tuple		=      prepare_image_inputs(self.image_processor_tester					,    equal_resolution=__lowerCamelCase					,    torchify=__lowerCamelCase      )

			UpperCamelCase	:Tuple		=      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      )

			UpperCamelCase	:int		=      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      )

			UpperCamelCase	:List[str]		=      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      ):
				UpperCamelCase	:Dict		=      image_processor(__lowerCamelCase					,    return_tensors="""pt"""      ).to(torch.bfloataa					,    """cpu"""      )

			# Try with text + image feature
			UpperCamelCase	:List[str]		=      image_processor(__lowerCamelCase					,    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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_					:		int           = {
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = [
	    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''UniSpeechSatForAudioFrameClassification''',
	    '''UniSpeechSatForCTC''',
	    '''UniSpeechSatForPreTraining''',
	    '''UniSpeechSatForSequenceClassification''',
	    '''UniSpeechSatForXVector''',
	    '''UniSpeechSatModel''',
	    '''UniSpeechSatPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_unispeech_sat import (
		    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    UniSpeechSatForAudioFrameClassification,
		    UniSpeechSatForCTC,
		    UniSpeechSatForPreTraining,
		    UniSpeechSatForSequenceClassification,
		    UniSpeechSatForXVector,
		    UniSpeechSatModel,
		    UniSpeechSatPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase_					:		str           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
