



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DeiTImageProcessor

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def __init__(       self							:			Any	,	a_							:			Tuple	,	a_							:			List[Any]=7	,	a_							:			Union[str, Any]=3	,	a_							:			str=18	,	a_							:			Optional[int]=30	,	a_							:			Tuple=4_00	,	a_							:			Tuple=True	,	a_							:			Optional[Any]=None	,	a_							:			Tuple=True	,	a_							:			Dict=None	,	a_							:			Optional[int]=True	,	a_							:			Union[str, Any]=[0.5, 0.5, 0.5]	,	a_							:			int=[0.5, 0.5, 0.5]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     size if size is not None else {'''height''': 20, '''width''': 20}
					__UpperCAmelCase   :					Any													=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

					__UpperCAmelCase   :					Optional[int]													=     parent
					__UpperCAmelCase   :					str													=     batch_size
					__UpperCAmelCase   :					Any													=     num_channels
					__UpperCAmelCase   :					int													=     image_size
					__UpperCAmelCase   :					int													=     min_resolution
					__UpperCAmelCase   :					Optional[Any]													=     max_resolution
					__UpperCAmelCase   :					int													=     do_resize
					__UpperCAmelCase   :					Tuple													=     size
					__UpperCAmelCase   :					int													=     do_center_crop
					__UpperCAmelCase   :					Optional[int]													=     crop_size
					__UpperCAmelCase   :					List[str]													=     do_normalize
					__UpperCAmelCase   :					List[str]													=     image_mean
					__UpperCAmelCase   :					List[Any]													=     image_std




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}

@require_torch
@require_vision
class 		UpperCAmelCase__      (      __UpperCamelCase						,unittest.TestCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	DeiTImageProcessor if is_vision_available() else None
			UpperCamelCase      						=	True




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     DeiTImageProcessingTester(self			)




			@property
			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					return self.image_processor_tester.prepare_image_processor_dict()




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(a_	,	'''do_resize'''			)			)
					self.assertTrue(hasattr(a_	,	'''size'''			)			)
					self.assertTrue(hasattr(a_	,	'''do_center_crop'''			)			)
					self.assertTrue(hasattr(a_	,	'''center_crop'''			)			)
					self.assertTrue(hasattr(a_	,	'''do_normalize'''			)			)
					self.assertTrue(hasattr(a_	,	'''image_mean'''			)			)
					self.assertTrue(hasattr(a_	,	'''image_std'''			)			)




			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size	,	{'''height''': 20, '''width''': 20}			)
					self.assertEqual(image_processor.crop_size	,	{'''height''': 18, '''width''': 18}			)

					__UpperCAmelCase   :					Dict													=     self.image_processing_class.from_dict(self.image_processor_dict	,	size=42	,	crop_size=84			)
					self.assertEqual(image_processor.size	,	{'''height''': 42, '''width''': 42}			)
					self.assertEqual(image_processor.crop_size	,	{'''height''': 84, '''width''': 84}			)




			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					pass




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__UpperCAmelCase   :					List[Any]													=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_			)
					for image in image_inputs:
							self.assertIsInstance(a_	,	Image.Image			)

					# Test not batched input
					__UpperCAmelCase   :					Any													=     image_processing(image_inputs[0]	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)

					# Test batched
					__UpperCAmelCase   :					Optional[Any]													=     image_processing(a_	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__UpperCAmelCase   :					Tuple													=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_	,	numpify=a_			)
					for image in image_inputs:
							self.assertIsInstance(a_	,	np.ndarray			)

					# Test not batched input
					__UpperCAmelCase   :					Dict													=     image_processing(image_inputs[0]	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)

					# Test batched
					__UpperCAmelCase   :					Dict													=     image_processing(a_	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__UpperCAmelCase   :					Optional[int]													=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_	,	torchify=a_			)
					for image in image_inputs:
							self.assertIsInstance(a_	,	torch.Tensor			)

					# Test not batched input
					__UpperCAmelCase   :					List[Any]													=     image_processing(image_inputs[0]	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)

					# Test batched
					__UpperCAmelCase   :					str													=     image_processing(a_	,	return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape	,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,	)










import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''			)

					if not os.path.isdir(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
							copyfile(self.vocab_file	,	a_			)

					return (out_vocab_file,)





