






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							_UpperCAmelCase     =       ViTImageProcessor if is_vision_available() else None
							@property
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_ (     self:      Tuple						)		->		Tuple:
													snake_case__									=     (3, 32, 1_28)
													snake_case__									=     tempfile.mkdtemp()

													# fmt: off
													snake_case__									=     ['[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
													snake_case__									=     dict(zip(UpperCamelCase       ,	range(len(UpperCamelCase						)						)						)						)

													snake_case__									=     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(UpperCamelCase						) + '\n'						)

													snake_case__									=     {
													    'do_normalize': False,
													    'do_resize': True,
													    'image_processor_type': 'ViTImageProcessor',
													    'resample': 3,
													    'size': {'height': 32, 'width': 1_28},
													}
													snake_case__									=     os.path.join(self.tmpdirname       ,	UpperCamelCase						)
													with open(self.image_processor_file       ,	'w'       ,	encoding='utf-8'						) as fp:
																			json.dump(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	**UpperCamelCase:      Tuple						)		->		int:
													return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	**UpperCamelCase:      int						)		->		int:
													return ViTImageProcessor.from_pretrained(self.tmpdirname       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int						)		->		Any:
													shutil.rmtree(self.tmpdirname						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[Any]:
													snake_case__									=     np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta						)

													snake_case__									=     Image.fromarray(np.moveaxis(UpperCamelCase       ,	0       ,	-1						)						)

													return image_input
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Union[str, Any]:
													snake_case__									=     self.get_tokenizer()
													snake_case__									=     self.get_image_processor()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)
													processor.save_pretrained(self.tmpdirname						)
													snake_case__									=     MgpstrProcessor.from_pretrained(self.tmpdirname       ,	use_fast=UpperCamelCase						)

													self.assertEqual(processor.char_tokenizer.get_vocab()       ,	tokenizer.get_vocab()						)
													self.assertIsInstance(processor.char_tokenizer       ,	UpperCamelCase						)

													self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor.to_json_string()						)
													self.assertIsInstance(processor.image_processor       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
													snake_case__									=     self.get_tokenizer()
													snake_case__									=     self.get_image_processor()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)
													processor.save_pretrained(self.tmpdirname						)

													snake_case__									=     self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'						)
													snake_case__									=     self.get_image_processor(do_normalize=UpperCamelCase       ,	padding_value=1.0						)

													snake_case__									=     MgpstrProcessor.from_pretrained(
													    self.tmpdirname       ,	bos_token='(BOS)'       ,	eos_token='(EOS)'       ,	do_normalize=UpperCamelCase       ,	padding_value=1.0						)

													self.assertEqual(processor.char_tokenizer.get_vocab()       ,	tokenizer_add_kwargs.get_vocab()						)
													self.assertIsInstance(processor.char_tokenizer       ,	UpperCamelCase						)

													self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor_add_kwargs.to_json_string()						)
													self.assertIsInstance(processor.image_processor       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     self.prepare_image_inputs()

													snake_case__									=     image_processor(UpperCamelCase       ,	return_tensors='np'						)
													snake_case__									=     processor(images=UpperCamelCase       ,	return_tensors='np'						)

													for key in input_image_proc.keys():
																			self.assertAlmostEqual(input_image_proc[key].sum()       ,	input_processor[key].sum()       ,	delta=1e-2						)
							def lowerCAmelCase_ (     self:      str						)		->		Any:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     'test'

													snake_case__									=     processor(text=UpperCamelCase						)

													snake_case__									=     tokenizer(UpperCamelCase						)
													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key]						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     'test'
													snake_case__									=     self.prepare_image_inputs()

													snake_case__									=     processor(text=UpperCamelCase       ,	images=UpperCamelCase						)

													self.assertListEqual(list(inputs.keys()						)       ,	['pixel_values', 'labels']						)

													# test if it raises when no input is passed
													with pytest.raises(UpperCamelCase						):
																			processor()
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

													snake_case__									=     processor.char_decode(UpperCamelCase						)
													snake_case__									=     tokenizer.batch_decode(UpperCamelCase						)
													snake_case__									=     [seq.replace(' '       ,	''						) for seq in decoded_tok]

													self.assertListEqual(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Any:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     None
													snake_case__									=     self.prepare_image_inputs()

													snake_case__									=     processor(text=UpperCamelCase       ,	images=UpperCamelCase						)

													self.assertListEqual(list(inputs.keys()						)       ,	processor.model_input_names						)


							def lowerCAmelCase_ (     self:      Dict						)		->		str:
													snake_case__									=     self.get_image_processor()
													snake_case__									=     self.get_tokenizer()

													snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

													snake_case__									=     torch.randn(1       ,	27       ,	38						)
													snake_case__									=     torch.randn(1       ,	27       ,	5_02_57						)
													snake_case__									=     torch.randn(1       ,	27       ,	3_05_22						)

													snake_case__									=     processor.batch_decode([char_input, bpe_input, wp_input]						)

													self.assertListEqual(list(results.keys()						)       ,	['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']						)












import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




