
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE 		=						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Any	 =    GPTSwaTokenizer
						__magic_name__:     List[str]	 =    False
						__magic_name__:     Any	 =    True
						__magic_name__:     Union[str, Any]	 =    False






						def UpperCAmelCase_			( self : Optional[int]		)							->      Dict:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A						,				eos_token='<unk>'						,				bos_token='<unk>'						,				pad_token='<unk>'		)

							tokenizer.save_pretrained(self.tmpdirname		)






						def UpperCAmelCase_			( self : int						,				_A : Any		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		'This is a test'
							snake_case_						:     Union[str, Any]     =		'This is a test'
							return input_text, output_text






						def UpperCAmelCase_			( self : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		'<s>'
							snake_case_						:     int     =		1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A		)						,				_A		)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A		)						,				_A		)






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     int     =		list(self.get_tokenizer().get_vocab().keys()		)

							self.assertEqual(vocab_keys[0]						,				'<unk>'		)
							self.assertEqual(vocab_keys[1]						,				'<s>'		)
							self.assertEqual(vocab_keys[-1]						,				'j'		)
							self.assertEqual(len(_A		)						,				2000		)






						def UpperCAmelCase_			( self : int		)							->      int:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size						,				2000		)






						def UpperCAmelCase_			( self : List[str]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A		)

							snake_case_						:     Union[str, Any]     =		tokenizer.tokenize('This is a test'		)
							self.assertListEqual(_A						,				['▁This', '▁is', '▁a', '▁t', 'est']		)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)						,				[465, 287, 265, 631, 842]		)

							snake_case_						:     Optional[int]     =		tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
							# fmt: off
							self.assertListEqual(
							    _A						,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						,				)
							# fmt: on

							snake_case_						:     Optional[int]     =		tokenizer.convert_tokens_to_ids(_A		)
							self.assertListEqual(
							    _A						,				[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]						,				)

							snake_case_						:     List[str]     =		tokenizer.convert_ids_to_tokens(_A		)
							# fmt: off
							self.assertListEqual(
							    _A						,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']		)
							# fmt: on






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







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A		)
							snake_case_						:     int     =		['This is a test', 'I was born in 92000, and this is falsé.']
							snake_case_						:     int     =		[
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(_A						,				_A		):
								self.assertListEqual(tokenizer.encode_fast(_A		)						,				_A		)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(_A						,				_A		):
								self.assertEqual(tokenizer.decode_fast(_A		)						,				_A		)






						@slow
						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		[
							    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
							    'Hey there, how are you doing this fine day?',
							    'This is a text with a trailing spaces followed by a dot     .',
							    'Häj sväjs lillebrör! =)',
							    'Det är inget fel på Mr. Cool',
							]

							# fmt: off
							snake_case_						:     Dict     =		{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=_A						,				model_name='AI-Sweden/gpt-sw3-126m'						,				sequences=_A						,				)








import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE 		=						"""docs/source/en/_toctree.yml"""


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[Any]     =		defaultdict(__a				)
	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[Any]     =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
		else:
			new_doc_list.append(__a				)

	snake_case_						:     Any     =		new_doc_list
	snake_case_						:     str     =		[key for key, value in counts.items() if value > 1]

	snake_case_						:     Any     =		[]
	for duplicate_key in duplicates:
		snake_case_						:     Any     =		list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
		if len(__a				) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
	snake_case_						:     str     =		sorted(__a				,	key=lambda __a				: s["title"].lower()				)

	# "overview" gets special treatment and is always first
	if len(__a				) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

	overview_doc.extend(__a				)

	# Sort
	return overview_doc


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     int     =		yaml.safe_load(f.read()				)

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

	# Then to the model doc
	snake_case_						:     Tuple     =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	snake_case_						:     Union[str, Any]     =		api_doc[scheduler_idx]['sections']
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	snake_case_						:     int     =		False
	if new_scheduler_doc != scheduler_doc:
		snake_case_						:     int     =		True
		if overwrite:
			snake_case_						:     Union[str, Any]     =		new_scheduler_doc

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


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		yaml.safe_load(f.read()				)

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

	# Then to the model doc
	snake_case_						:     List[Any]     =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	snake_case_						:     Dict     =		False
	snake_case_						:     Union[str, Any]     =		api_doc[pipeline_idx]['sections']
	snake_case_						:     Union[str, Any]     =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			snake_case_						:     Optional[Any]     =		pipeline_doc['section']
			snake_case_						:     Optional[int]     =		clean_doc_toc(__a				)
			if overwrite:
				snake_case_						:     Tuple     =		new_sub_pipeline_doc
		new_pipeline_docs.append(__a				)

	# sort overall pipeline doc
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	if new_pipeline_docs != pipeline_docs:
		snake_case_						:     List[str]     =		True
		if overwrite:
			snake_case_						:     List[str]     =		new_pipeline_docs

	if diff:
		if overwrite:
			snake_case_						:     List[Any]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		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_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)

