"""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  lowerCAmelCase__     (      _UpperCamelCase							:					List[Any]			,      _UpperCamelCase							:					str=False			,      _UpperCamelCase							:					Optional[Any]=False			,      _UpperCamelCase							:					Any=False       )       ->					Union[str, Any]:





						"""simple docstring"""






						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(_UpperCamelCase			,      'size_divisor'			,      _UpperCamelCase       ):
																								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																								snake_case             =      max(image_processor_tester.size_divisor			,      _UpperCamelCase       )
																		snake_case       ,snake_case             =      np.random.choice(np.arange(_UpperCamelCase			,      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(_UpperCamelCase			,      0			,      -1       )       ) for image in image_inputs]

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

						return image_inputs





def  lowerCAmelCase__     (      _UpperCamelCase							:					Dict			,      _UpperCamelCase							:					Dict=1_0			,      _UpperCamelCase							:					Optional[int]=1_0			,      _UpperCamelCase							:					Any=False			,      _UpperCamelCase							:					Tuple=False       )       ->					Tuple:





						"""simple docstring"""






						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(_UpperCamelCase			,      0			,      -1       )       ) for frame in video]

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

						return video





def  lowerCAmelCase__     (      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					Optional[Any]=False			,      _UpperCamelCase							:					int=False			,      _UpperCamelCase							:					List[str]=False       )       ->					str:





						"""simple docstring"""






						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=_UpperCamelCase			,      width=_UpperCamelCase			,      height=_UpperCamelCase			,      numpify=_UpperCamelCase			,      torchify=_UpperCamelCase			,      )
												video_inputs.append(_UpperCamelCase       )

						return video_inputs



class 	lowerCAmelCase_     :



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    None

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )

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

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )

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

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class()
										self.assertIsNotNone(lowerCAmelCase     )

				@require_torch
				@require_vision
				def 		snake_case			(					self     ):






										"""simple docstring"""

										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=lowerCAmelCase		,     torchify=lowerCAmelCase     )

																snake_case             =      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     )

																snake_case             =      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     )

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

																# Try with text + image feature
																snake_case             =      image_processor(lowerCAmelCase		,     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     )



"""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,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MarianForCausalLM",
					    "MarianModel",
					    "MarianMTModel",
					    "MarianPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

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


