



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		str      							=     None
					A__					:		str      							=     BloomTokenizerFast
					A__					:		List[str]      							=     BloomTokenizerFast
					A__					:		Union[str, Any]      							=     True
					A__					:		int      							=     False
					A__					:		List[Any]      							=     "tokenizer_file"
					A__					:		Optional[Any]      							=     {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								super().setUp()
								A__	  =     BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
								tokenizer.save_pretrained(self.tmpdirname       )



					def      _a				(  self       :							Optional[int]					,       **_snake_case       :							Dict       ):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map       )
								return BloomTokenizerFast.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_rust_tokenizer()

								A__	  =     ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
								A__	  =     [[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

								A__	  =     tokenizer.batch_encode_plus(_snake_case       )['input_ids']
								self.assertListEqual(_snake_case					,       _snake_case       )

								A__	  =     tokenizer.batch_decode(_snake_case       )
								self.assertListEqual(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							Dict=6       ):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
														A__	  =     self.rust_tokenizer_class.from_pretrained(_snake_case					,       **_snake_case       )
														# tokenizer_r.pad_token = None # Hotfixing padding = None
														# Simple input
														A__	  =     'This is a simple input'
														A__	  =     ['This is a simple input 1', 'This is a simple input 2']
														A__	  =     ('This is a simple input', 'This is a pair')
														A__	  =     [
														    ('This is a simple input 1', 'This is a simple input 2'),
														    ('This is a simple pair 1', 'This is a simple pair 2'),
														]

														# Simple input tests
														try:
																	tokenizer_r.encode(_snake_case					,       max_length=_snake_case       )
																	tokenizer_r.encode_plus(_snake_case					,       max_length=_snake_case       )

																	tokenizer_r.batch_encode_plus(_snake_case					,       max_length=_snake_case       )
																	tokenizer_r.encode(_snake_case					,       max_length=_snake_case       )
																	tokenizer_r.batch_encode_plus(_snake_case					,       max_length=_snake_case       )
														except ValueError:
																	self.fail('Bloom Tokenizer should be able to deal with padding'       )

														A__	  =     None  # Hotfixing padding = None
														self.assertRaises(_snake_case					,       tokenizer_r.encode					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'       )

														# Simple input
														self.assertRaises(_snake_case					,       tokenizer_r.encode_plus					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'       )

														# Simple input
														self.assertRaises(
														    _snake_case					,       tokenizer_r.batch_encode_plus					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'					,       )

														# Pair input
														self.assertRaises(_snake_case					,       tokenizer_r.encode					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'       )

														# Pair input
														self.assertRaises(_snake_case					,       tokenizer_r.encode_plus					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'       )

														# Pair input
														self.assertRaises(
														    _snake_case					,       tokenizer_r.batch_encode_plus					,       _snake_case					,       max_length=_snake_case					,       padding='max_length'					,       )



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     self.get_rust_tokenizer()
								A__	  =     load_dataset('xnli'					,       'all_languages'					,       split='test'					,       streaming=_snake_case       )

								A__	  =     next(iter(_snake_case       )       )['premise']  # pick up one data
								A__	  =     list(sample_data.values()       )

								A__	  =     list(map(tokenizer.encode					,       _snake_case       )       )
								A__	  =     [tokenizer.decode(_snake_case					,       clean_up_tokenization_spaces=_snake_case       ) for x in output_tokens]
								self.assertListEqual(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )					,       1       )
								self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )					,       1       )









import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__   			=   '''docs/source/en/_toctree.yml'''


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     defaultdict(__UpperCamelCase       )
			for doc in model_doc:
						counts[doc["local"]] += 1
			A__	  =     [key for key, value in counts.items() if value > 1]

			A__	  =     []
			for duplicate_key in duplicates:
						A__	  =     list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}       )
						if len(__UpperCamelCase       ) > 1:
									raise ValueError(
									    f'''{duplicate_key} is present several times in the documentation table of content at '''
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.'       )
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]       )

			# Sort
			return sorted(__UpperCamelCase			,						key=lambda __UpperCamelCase       : s["title"].lower()       )


def A    (							__UpperCamelCase=False       )    ->   str:
			with open(__UpperCamelCase			,						encoding='utf-8'       ) as f:
						A__	  =     yaml.safe_load(f.read()       )

			# Get to the API doc
			A__	  =     0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			A__	  =     content[api_idx]['sections']

			# Then to the model doc
			A__	  =     0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			A__	  =     api_doc[model_idx]['sections']

			A__	  =     [(idx, section) for idx, section in enumerate(__UpperCamelCase       ) if 'sections' in section]
			A__	  =     False
			for idx, modality_doc in modalities_docs:
						A__	  =     modality_doc['sections']
						A__	  =     clean_model_doc_toc(__UpperCamelCase       )

						if old_modality_doc != new_modality_doc:
									A__	  =     True
									if overwrite:
												A__	  =     new_modality_doc

			if diff:
						if overwrite:
									A__	  =     model_doc
									A__	  =     api_doc
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as f:
												f.write(yaml.dump(__UpperCamelCase			,						allow_unicode=__UpperCamelCase       )       )
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




