



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


_lowercase: int      			=		logging.get_logger(__name__)



class 	_lowercase							(   __UpperCAmelCase	):





	"""simple docstring"""
	__A							=							['pixel_values']




	def __init__(self		,       lowerCamelCase_ = True		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = PILImageResampling.BILINEAR		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 1 / 255		,       lowerCamelCase_ = True		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						a					     =   size if size is not None else {"""shortest_edge""": 384}
						a					     =   get_size_dict(lowerCamelCase_		,       default_to_square=lowerCamelCase_		)

						a					     =   do_resize
						a					     =   size
						# Default value set here for backwards compatibility where the value in config is None
						a					     =   crop_pct if crop_pct is not None else 224 / 256
						a					     =   resample
						a					     =   do_rescale
						a					     =   rescale_factor
						a					     =   do_normalize
						a					     =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						a					     =   image_std if image_std is not None else IMAGENET_STANDARD_STD




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = PILImageResampling.BICUBIC		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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

						if shortest_edge < 384:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											a					     =   int(shortest_edge / crop_pct		)
											a					     =   get_resize_output_image_size(lowerCamelCase_		,       size=lowerCamelCase_		,       default_to_square=lowerCamelCase_		)
											a					     =   resize(image=lowerCamelCase_		,       size=lowerCamelCase_		,       resample=lowerCamelCase_		,       data_format=lowerCamelCase_		,       **lowerCamelCase_		)
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=lowerCamelCase_		,       size=(shortest_edge, shortest_edge)		,       data_format=lowerCamelCase_		,       **lowerCamelCase_		)
						else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    lowerCamelCase_		,       size=(shortest_edge, shortest_edge)		,       resample=lowerCamelCase_		,       data_format=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = ChannelDimension.FIRST		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   do_resize if do_resize is not None else self.do_resize
						a					     =   crop_pct if crop_pct is not None else self.crop_pct
						a					     =   resample if resample is not None else self.resample
						a					     =   do_rescale if do_rescale is not None else self.do_rescale
						a					     =   rescale_factor if rescale_factor is not None else self.rescale_factor
						a					     =   do_normalize if do_normalize is not None else self.do_normalize
						a					     =   image_mean if image_mean is not None else self.image_mean
						a					     =   image_std if image_std is not None else self.image_std

						a					     =   size if size is not None else self.size
						a					     =   get_size_dict(lowerCamelCase_		,       default_to_square=lowerCamelCase_		)

						a					     =   make_list_of_images(lowerCamelCase_		)

						if not valid_images(lowerCamelCase_		):
											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.
						a					     =   [to_numpy_array(lowerCamelCase_		) for image in images]

						if do_resize:
											a					     =   [self.resize(image=lowerCamelCase_		,       size=lowerCamelCase_		,       crop_pct=lowerCamelCase_		,       resample=lowerCamelCase_		) for image in images]

						if do_rescale:
											a					     =   [self.rescale(image=lowerCamelCase_		,       scale=lowerCamelCase_		) for image in images]

						if do_normalize:
											a					     =   [self.normalize(image=lowerCamelCase_		,       mean=lowerCamelCase_		,       std=lowerCamelCase_		) for image in images]

						a					     =   [to_channel_dimension_format(lowerCamelCase_		,       lowerCamelCase_		) for image in images]

						a					     =   {"""pixel_values""": images}
						return BatchFeature(data=lowerCamelCase_		,       tensor_type=lowerCamelCase_		)





'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=16       ,      lowercase=2       ,      lowercase=0.02       ,      lowercase=4       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						str	=     batch_size
				A_	:						List[Any]	=     seq_length
				A_	:						Dict	=     is_training
				A_	:						List[Any]	=     use_attention_mask
				A_	:						Any	=     use_token_type_ids
				A_	:						Optional[int]	=     use_labels
				A_	:						Tuple	=     vocab_size
				A_	:						List[str]	=     hidden_size
				A_	:						List[str]	=     num_hidden_layers
				A_	:						Optional[Any]	=     num_attention_heads
				A_	:						int	=     intermediate_size
				A_	:						Optional[Any]	=     hidden_act
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[Any]	=     attention_probs_dropout_prob
				A_	:						Any	=     max_position_embeddings
				A_	:						Union[str, Any]	=     type_vocab_size
				A_	:						int	=     type_sequence_label_size
				A_	:						Any	=     initializer_range
				A_	:						List[str]	=     num_choices


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Any	=     None
				if self.use_attention_mask:
							A_	:						Any	=     random_attention_mask([self.batch_size, self.seq_length]							)

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

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