

'''simple docstring'''

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      __magic_name__							(			A       , A=False       , A=False       , A=False				)      ->		List[Any]:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	snake_case							=					[]
	for i in range(image_processor_tester.batch_size				):
		if equal_resolution:
			snake_case							=					snake_case							=					image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			snake_case							=					image_processor_tester.min_resolution
			if getattr(A       , 'size_divisor'       , A				):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				snake_case							=					max(image_processor_tester.size_divisor       , A				)
			snake_case			,			snake_case							=					np.random.choice(np.arange(A       , 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
		snake_case							=					[Image.fromarray(np.moveaxis(A       , 0       , -1				)				) for image in image_inputs]

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

	return image_inputs

def      __magic_name__							(			A       , A=1_0       , A=1_0       , A=False       , A=False				)      ->		List[Any]:
	snake_case							=					[]
	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
		snake_case							=					[Image.fromarray(np.moveaxis(A       , 0       , -1				)				) for frame in video]

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

	return video

def      __magic_name__							(			A       , A=False       , A=False       , A=False				)      ->		Union[str, Any]:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

	return video_inputs







class lowerCamelCase   :
				snake_case_							     = None

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					snake_case							=					json.loads(image_processor.to_json_string() )
					for key, value in self.image_processor_dict.items():
						self.assertEqual(obj[key],						lowercase_ )

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

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

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

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )

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

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

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class()
					self.assertIsNotNone(lowercase_ )






				@require_torch
				@require_vision
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					if self.test_cast_dtype is not None:
						# Initialize image_processor
						snake_case							=					self.image_processing_class(**self.image_processor_dict )

						# create random PyTorch tensors
						snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						torchify=lowercase_ )

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

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

						snake_case							=					image_processor(lowercase_,						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(lowercase_ ):
							snake_case							=					image_processor(lowercase_,						return_tensors='pt' ).to(torch.bfloataa,						'cpu' )

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


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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