
'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


UpperCAmelCase__ :			int											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  ['pixel_values']





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = 0.9   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							Union[int, float] = 1 / 255   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)
				lowerCAmelCase__										=				size if size is not None else {"shortest_edge": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				crop_pct
				lowerCAmelCase__										=				resample
				lowerCAmelCase__										=				do_center_crop
				lowerCAmelCase__										=				crop_size
				lowerCAmelCase__										=				do_rescale
				lowerCAmelCase__										=				rescale_factor
				lowerCAmelCase__										=				do_normalize
				lowerCAmelCase__										=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				lowerCAmelCase__										=				image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[float] = None   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Optional[int]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
				if crop_pct is not None:
							if "shortest_edge" in size:
										lowerCAmelCase__										=				int(size["shortest_edge"] / crop_pct				)
							elif "height" in size and "width" in size:
										if size["height"] == size["width"]:
													lowerCAmelCase__										=				int(size["height"] / crop_pct				)
										else:
													lowerCAmelCase__										=				(int(size["height"] / crop_pct				), int(size["width"] / crop_pct				))
							else:
										raise ValueError("Invalid size for resize: {}".format(__magic_name__				)				)

							lowerCAmelCase__										=				get_resize_output_image_size(__magic_name__   ,				size=__magic_name__   ,				default_to_square=__magic_name__				)
				else:
							if "shortest_edge" in size:
										lowerCAmelCase__										=				get_resize_output_image_size(__magic_name__   ,				size=size["shortest_edge"]   ,				default_to_square=__magic_name__				)
							elif "height" in size and "width" in size:
										lowerCAmelCase__										=				(size["height"], size["width"])
							else:
										raise ValueError("Invalid size for resize: {}".format(__magic_name__				)				)

				return resize(__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""				)
				return center_crop(__magic_name__   ,				size=(size["height"], size["width"])   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[int, float]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							str   ,				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							ImageInput   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = None   ,				__magic_name__      :							PILImageResampling = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							float = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[str, TensorType]] = None   ,				__magic_name__      :							ChannelDimension = ChannelDimension.FIRST   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

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

				lowerCAmelCase__										=				size if size is not None else self.size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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_center_crop and crop_pct is None:
							raise ValueError("Crop_pct must be specified if do_center_crop is True."				)

				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.
				lowerCAmelCase__										=				[to_numpy_array(__magic_name__				) for image in images]

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

				if do_center_crop:
							lowerCAmelCase__										=				[self.center_crop(image=__magic_name__   ,				size=__magic_name__				) for image in images]

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

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

				lowerCAmelCase__										=				[to_channel_dimension_format(__magic_name__   ,				__magic_name__				) for image in images]

				lowerCAmelCase__										=				{"pixel_values": images}
				return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__				)












import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     NystromformerForMaskedLM,
     NystromformerForMultipleChoice,
     NystromformerForQuestionAnswering,
     NystromformerForSequenceClassification,
     NystromformerForTokenClassification,
     NystromformerModel,
 )
 from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=7				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=99				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=16				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=4				,     snake_case_=None				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							seq_length
         _A     =							is_training
         _A     =							use_input_mask
         _A     =							use_token_type_ids
         _A     =							use_labels
         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							type_vocab_size
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							num_labels
         _A     =							num_choices
         _A     =							scope




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

         _A     =							None
         if self.use_input_mask:
             _A     =							random_attention_mask([self.batch_size, self.seq_length]							)

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

         _A     =							None
         _A     =							None
         _A     =							None
         if self.use_labels:
             _A     =							ids_tensor([self.batch_size]				,     self.type_sequence_label_size							)
             _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels							)
             _A     =							ids_tensor([self.batch_size]				,     self.num_choices							)

         _A     =							self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         return NystromformerConfig(
             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=snake_case_				,     initializer_range=self.initializer_range				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForMaskedLM(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForQuestionAnswering(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     start_positions=snake_case_				,     end_positions=snake_case_				,     )
         self.parent.assertEqual(result.start_logits.shape				,     (self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.end_logits.shape				,     (self.batch_size, self.seq_length)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForSequenceClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForTokenClassification(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_choices
         _A     =							NystromformerForMultipleChoice(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							input_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							token_type_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							input_mask.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_				,     )
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': NystromformerModel,
             'fill-mask': NystromformerForMaskedLM,
             'question-answering': NystromformerForQuestionAnswering,
             'text-classification': NystromformerForSequenceClassification,
             'token-classification': NystromformerForTokenClassification,
             'zero-shot': NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             _A     =							type
             self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							NystromformerModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)







@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModel.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							torch.tensor([[0, 1, 2, 3, 4, 5]]							)

         with torch.no_grad():
             _A     =							model(snake_case_							)[0]

         _A     =							torch.Size((1, 6, 768)							)
         self.assertEqual(output.shape				,     snake_case_							)

         _A     =							torch.tensor(
             [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]							)

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




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							'the [MASK] of Belgium is Brussels'

         _A     =							AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'							)

         _A     =							tokenizer(snake_case_				,     return_tensors='pt'							)

         with torch.no_grad():
             _A     =							model(encoding.input_ids							).logits

         _A     =							token_logits[:, 2, :].argmax(-1							)[0]

         self.assertEqual(tokenizer.decode(snake_case_							)				,     'capital'							)





