import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor







class     __lowerCAmelCase					(    unittest.TestCase		):



			def __init__(		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						Optional[Any]=7				,	A     :						Union[str, Any]=3				,	A     :						Any=18				,	A     :						Union[str, Any]=30				,	A     :						Optional[int]=4_00				,	A     :						Tuple=True				,	A     :						Any=None				,	A     :						int=True				,	A     :						Any=False				,	A     :						Any=True				,	A     :						Any=True				,	A     :						int=[0.5, 0.5, 0.5]				,	A     :						List[str]=[0.5, 0.5, 0.5]				,	)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     min_resolution
						_UpperCAmelCase	 =     max_resolution
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size if size is not None else {'height': 18, 'width': 20}
						_UpperCAmelCase	 =     do_thumbnail
						_UpperCAmelCase	 =     do_align_axis
						_UpperCAmelCase	 =     do_pad
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean
						_UpperCAmelCase	 =     image_std



			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}







@require_torch
@require_vision
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  DonutImageProcessor if is_vision_available() else None



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     DonutImageProcessingTester(self)



			@property
			def 					_lowerCamelCase (		self     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def 					_lowerCamelCase (		self     :						Any)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A				,	'do_resize'))
						self.assertTrue(hasattr(A				,	'size'))
						self.assertTrue(hasattr(A				,	'do_thumbnail'))
						self.assertTrue(hasattr(A				,	'do_align_long_axis'))
						self.assertTrue(hasattr(A				,	'do_pad'))
						self.assertTrue(hasattr(A				,	'do_normalize'))
						self.assertTrue(hasattr(A				,	'image_mean'))
						self.assertTrue(hasattr(A				,	'image_std'))



			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[int]:



						"""simple docstring"""






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

						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=42)
						self.assertEqual(image_processor.size				,	{'height': 42, 'width': 42})

						# Previous config had dimensions in (width, height) order
						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=(42, 84))
						self.assertEqual(image_processor.size				,	{'height': 84, 'width': 42})



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



						"""simple docstring"""






						pass



			@is_flaky()
			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase	 =     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	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)



			@is_flaky()
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase	 =     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	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)



			@is_flaky()
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase	 =     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	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MgpstrTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



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



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
						# fmt: on
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						with open(self.vocab_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'tester'
						_UpperCAmelCase	 =     'tester'
						return input_text, output_text



			@unittest.skip('MGP-STR always lower cases letters.')
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers(do_lower_case=A)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase	 =     '[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token})
												_UpperCAmelCase	 =     tokenizer.encode([special_token]				,	add_special_tokens=A)
												self.assertEqual(len(A)				,	1)

												_UpperCAmelCase	 =     tokenizer.decode(A				,	skip_special_tokens=A)
												self.assertTrue(special_token not in decoded)



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_input_output_texts(A)

												_UpperCAmelCase	 =     tokenizer.tokenize(A)
												_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
												_UpperCAmelCase	 =     tokenizer.encode(A				,	add_special_tokens=A)
												self.assertListEqual(A				,	A)

												_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
												self.assertNotEqual(len(A)				,	0)
												_UpperCAmelCase	 =     tokenizer.decode(A)
												self.assertIsInstance(A				,	A)

												self.assertEqual(text_a.replace(' '				,	'')				,	A)



			@unittest.skip('MGP-STR tokenizer only handles one sequence.')
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						pass



			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
			def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



						"""simple docstring"""






						pass
