



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_A				:    List[str]     						=logging.get_logger(__name__)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						["""pixel_values"""]




					def __init__(  self				:	Optional[int]     ,  UpperCamelCase_				:	bool = True     ,  UpperCamelCase_				:	Dict[str, int] = None     ,  UpperCamelCase_				:	float = None     ,  UpperCamelCase_				:	PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase_				:	bool = True     ,  UpperCamelCase_				:	Union[int, float] = 1 / 255     ,  UpperCamelCase_				:	bool = True     ,  UpperCamelCase_				:	Optional[Union[float, List[float]]] = None     ,  UpperCamelCase_				:	Optional[Union[float, List[float]]] = None     ,  **UpperCamelCase_				:	str     ,  )  ->       None:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		size if size is not None else {'shortest_edge': 384}
						_lowercase					:       Union[str, Any]       =		get_size_dict(UpperCamelCase_     ,  default_to_square=UpperCamelCase_					)

						_lowercase					:       Any       =		do_resize
						_lowercase					:       List[str]       =		size
						# Default value set here for backwards compatibility where the value in config is None
						_lowercase					:       Dict       =		crop_pct if crop_pct is not None else 224 / 256
						_lowercase					:       Tuple       =		resample
						_lowercase					:       str       =		do_rescale
						_lowercase					:       str       =		rescale_factor
						_lowercase					:       List[str]       =		do_normalize
						_lowercase					:       str       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_lowercase					:       List[Any]       =		image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	np.ndarray     ,  UpperCamelCase_				:	Dict[str, int]     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase_				:	Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase_				:	str     ,  )  ->       np.ndarray:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		get_size_dict(UpperCamelCase_     ,  default_to_square=UpperCamelCase_					)
						if "shortest_edge" not in size:
							raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''					)
						_lowercase					:       str       =		size['shortest_edge']

						if shortest_edge < 384:
							# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
							_lowercase					:       List[Any]       =		int(shortest_edge / crop_pct					)
							_lowercase					:       Tuple       =		get_resize_output_image_size(UpperCamelCase_     ,  size=UpperCamelCase_     ,  default_to_square=UpperCamelCase_					)
							_lowercase					:       Union[str, Any]       =		resize(image=UpperCamelCase_     ,  size=UpperCamelCase_     ,  resample=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
							# then crop to (shortest_edge, shortest_edge)
							return center_crop(image=UpperCamelCase_     ,  size=(shortest_edge, shortest_edge)     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
						else:
							# warping (no cropping) when evaluated at 384 or larger
							return resize(
							    UpperCamelCase_     ,  size=(shortest_edge, shortest_edge)     ,  resample=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	np.ndarray     ,  UpperCamelCase_				:	Union[int, float]     ,  UpperCamelCase_				:	Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase_				:	Any     ,  )  ->       Dict:



						'''simple docstring'''





						return rescale(UpperCamelCase_     ,  scale=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	np.ndarray     ,  UpperCamelCase_				:	Union[float, List[float]]     ,  UpperCamelCase_				:	Union[float, List[float]]     ,  UpperCamelCase_				:	Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase_				:	Dict     ,  )  ->       np.ndarray:



						'''simple docstring'''





						return normalize(UpperCamelCase_     ,  mean=UpperCamelCase_     ,  std=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	ImageInput     ,  UpperCamelCase_				:	bool = None     ,  UpperCamelCase_				:	Dict[str, int] = None     ,  UpperCamelCase_				:	float = None     ,  UpperCamelCase_				:	PILImageResampling = None     ,  UpperCamelCase_				:	bool = None     ,  UpperCamelCase_				:	float = None     ,  UpperCamelCase_				:	bool = None     ,  UpperCamelCase_				:	Optional[Union[float, List[float]]] = None     ,  UpperCamelCase_				:	Optional[Union[float, List[float]]] = None     ,  UpperCamelCase_				:	Optional[Union[str, TensorType]] = None     ,  UpperCamelCase_				:	ChannelDimension = ChannelDimension.FIRST     ,  **UpperCamelCase_				:	int     ,  )  ->       PIL.Image.Image:



						'''simple docstring'''





						_lowercase					:       Any       =		do_resize if do_resize is not None else self.do_resize
						_lowercase					:       int       =		crop_pct if crop_pct is not None else self.crop_pct
						_lowercase					:       Dict       =		resample if resample is not None else self.resample
						_lowercase					:       Dict       =		do_rescale if do_rescale is not None else self.do_rescale
						_lowercase					:       int       =		rescale_factor if rescale_factor is not None else self.rescale_factor
						_lowercase					:       List[Any]       =		do_normalize if do_normalize is not None else self.do_normalize
						_lowercase					:       Optional[int]       =		image_mean if image_mean is not None else self.image_mean
						_lowercase					:       Union[str, Any]       =		image_std if image_std is not None else self.image_std

						_lowercase					:       str       =		size if size is not None else self.size
						_lowercase					:       List[str]       =		get_size_dict(UpperCamelCase_     ,  default_to_square=UpperCamelCase_					)

						_lowercase					:       Any       =		make_list_of_images(UpperCamelCase_					)

						if not valid_images(UpperCamelCase_					):
							raise ValueError(
							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
							    'torch.Tensor, tf.Tensor or jax.ndarray.'					)

						if do_resize and size is None or resample is None:
							raise ValueError('Size and resample must be specified if do_resize is True.'					)

						if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
							raise ValueError('crop_pct must be specified if size < 384.'					)

						if do_rescale and rescale_factor is None:
							raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

						# All transformations expect numpy arrays.
						_lowercase					:       int       =		[to_numpy_array(UpperCamelCase_					) for image in images]

						if do_resize:
							_lowercase					:       int       =		[self.resize(image=UpperCamelCase_     ,  size=UpperCamelCase_     ,  crop_pct=UpperCamelCase_     ,  resample=UpperCamelCase_					) for image in images]

						if do_rescale:
							_lowercase					:       List[str]       =		[self.rescale(image=UpperCamelCase_     ,  scale=UpperCamelCase_					) for image in images]

						if do_normalize:
							_lowercase					:       Any       =		[self.normalize(image=UpperCamelCase_     ,  mean=UpperCamelCase_     ,  std=UpperCamelCase_					) for image in images]

						_lowercase					:       Optional[Any]       =		[to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					) for image in images]

						_lowercase					:       Optional[int]       =		{'pixel_values': images}
						return BatchFeature(data=UpperCamelCase_     ,  tensor_type=UpperCamelCase_					)





'''simple docstring'''

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=13     ,  UpperCamelCase_				:	Union[str, Any]=7     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Tuple=32     ,  UpperCamelCase_				:	List[str]=5     ,  UpperCamelCase_				:	Dict=4     ,  UpperCamelCase_				:	Tuple=37     ,  UpperCamelCase_				:	int="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Union[str, Any]=4     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       int       =		parent
						_lowercase					:       str       =		batch_size
						_lowercase					:       List[str]       =		seq_length
						_lowercase					:       Dict       =		is_training
						_lowercase					:       Optional[int]       =		use_attention_mask
						_lowercase					:       List[Any]       =		use_token_type_ids
						_lowercase					:       Union[str, Any]       =		use_labels
						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       List[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Optional[int]       =		intermediate_size
						_lowercase					:       Any       =		hidden_act
						_lowercase					:       List[str]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Optional[int]       =		max_position_embeddings
						_lowercase					:       int       =		type_vocab_size
						_lowercase					:       Any       =		type_sequence_label_size
						_lowercase					:       Any       =		initializer_range
						_lowercase					:       str       =		num_choices




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       int       =		None
						if self.use_attention_mask:
							_lowercase					:       Optional[int]       =		random_attention_mask([self.batch_size, self.seq_length]					)

						_lowercase					:       Any       =		None
						if self.use_token_type_ids:
							_lowercase					:       Optional[Any]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

						_lowercase					:       str       =		RoFormerConfig(
						    vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  is_decoder=UpperCamelCase_     ,  initializer_range=self.initializer_range     ,  )

						return config, input_ids, token_type_ids, attention_mask




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.prepare_config_and_inputs()
						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Optional[int]       =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict






@require_flax
class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						True

					A_						    =						(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		FlaxRoFormerModelTester(self					)




					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						for model_class_name in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'     ,  from_pt=UpperCamelCase_					)
							_lowercase					:       str       =		model(np.ones((1, 1)					)					)
							self.assertIsNotNone(UpperCamelCase_					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
						_lowercase					:       Any       =		jnp.array([[0, 1, 2, 3, 4, 5]]					)
						_lowercase					:       int       =		model(UpperCamelCase_					)[0]

						_lowercase					:       Union[str, Any]       =		5_0000

						_lowercase					:       str       =		(1, 6, vocab_size)
						self.assertEqual(output.shape     ,  UpperCamelCase_					)

						_lowercase					:       int       =		jnp.array(
						    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

						self.assertTrue(jnp.allclose(output[:, :3, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)					)
