




import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = GPTNeoXJapaneseTokenizer
					_lowerCamelCase		  = False
					_lowerCamelCase		  = {"""do_clean_text""": False, """add_prefix_space""": False}

					def 	lowercase_							(				self					):







						'''simple docstring'''




						super().setUp()

						lowerCamelCase_							:		Optional[int]							   =							[
						    'こん',
						    'こんに',
						    'にちは',
						    'ばんは',
						    '世界,㔺界',
						    '、',
						    '。',
						    '<BR>',
						    '<SP>',
						    '<TAB>',
						    '<URL>',
						    '<EMAIL>',
						    '<TEL>',
						    '<DATE>',
						    '<PRICE>',
						    '<BLOCK>',
						    '<KIGOU>',
						    '<U2000U2BFF>',
						    '<|emoji1|>',
						    '<unk>',
						    '<|startoftext|>',
						    '<|endoftext|>',
						]
						lowerCamelCase_							:		Any							   =							{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
						lowerCamelCase_							:		Any							   =							{'unk_token': '<unk>'}

						lowerCamelCase_							:		str							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['vocab_file']					)
						lowerCamelCase_							:		Dict							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['emoji_file']					)
						with open(self.vocab_file ,       'w' ,       encoding='utf-8'					) as vocab_writer:
							vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)
						with open(self.emoji_file ,       'w'					) as emoji_writer:
							emoji_writer.write(json.dumps(__lowercase					)					)

					def 	lowercase_							(				self ,       **__lowercase					):







						'''simple docstring'''




						kwargs.update(self.special_tokens_map					)
						return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname ,       **__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							'こんにちは、世界。 \nこんばんは、㔺界。😀'
						lowerCamelCase_							:		Union[str, Any]							   =							'こんにちは、世界。 \nこんばんは、世界。😀'
						return input_text, output_text

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							self.get_input_output_texts(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.encode(__lowercase ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							tokenizer.decode(__lowercase ,       clean_up_tokenization_spaces=__lowercase					)
						return text, ids

					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass  # TODO add if relevant

					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass  # TODO add if relevant

					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass  # TODO add if relevant

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							self.get_tokenizer()

						# Testing tokenization
						lowerCamelCase_							:		Optional[Any]							   =							'こんにちは、世界。　こんばんは、㔺界。'
						lowerCamelCase_							:		str							   =							['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
						lowerCamelCase_							:		Tuple							   =							tokenizer.tokenize(__lowercase					)
						self.assertListEqual(__lowercase ,       __lowercase					)

						# Testing conversion to ids without special tokens
						lowerCamelCase_							:		Tuple							   =							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						lowerCamelCase_							:		List[Any]							   =							tokenizer.convert_tokens_to_ids(__lowercase					)
						self.assertListEqual(__lowercase ,       __lowercase					)

						# Testing conversion to ids with special tokens
						lowerCamelCase_							:		List[str]							   =							tokens + [tokenizer.unk_token]
						lowerCamelCase_							:		List[Any]							   =							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						lowerCamelCase_							:		int							   =							tokenizer.convert_tokens_to_ids(__lowercase					)
						self.assertListEqual(__lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'					)

						lowerCamelCase_							:		List[Any]							   =							tokenizer.encode('ありがとう。' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Tuple							   =							tokenizer.encode('どういたしまして。' ,       add_special_tokens=__lowercase					)

						lowerCamelCase_							:		List[Any]							   =							tokenizer.build_inputs_with_special_tokens(__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							tokenizer.build_inputs_with_special_tokens(__lowercase ,       __lowercase					)

						assert encoded_sentence == ids_a
						assert encoded_pair == ids_a + ids_a

					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass





					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass






import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase__              =						'src/diffusers'
UpperCamelCase__              =						'docs/source/en'
UpperCamelCase__              =						'.'






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


	with open(A						,   'r'						,   encoding='utf-8'						,   newline='\n'			) as f:
		lowerCamelCase_							:		int							   =							f.readlines()
	# Find the start prompt.
	lowerCamelCase_							:		Tuple							   =							0
	while not lines[start_index].startswith(A			):
		start_index += 1
	start_index += 1

	lowerCamelCase_							:		Tuple							   =							start_index
	while not lines[end_index].startswith(A			):
		end_index += 1
	end_index -= 1

	while len(lines[start_index]			) <= 1:
		start_index += 1
	while len(lines[end_index]			) <= 1:
		end_index -= 1
	end_index += 1
	return "".join(lines[start_index:end_index]			), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCamelCase__              =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__              =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase__              =						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase__              =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase__              =						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
UpperCamelCase__              =						spec.loader.load_module()






def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,   A			)
	return [m.group(0			) for m in matches]






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							2 if text == '✅' or text == '❌' else len(A			)
	lowerCamelCase_							:		List[str]							   =							(width - text_length) // 2
	lowerCamelCase_							:		int							   =							width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					snake_case					(							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowerCamelCase_							:		Optional[Any]							   =							{
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	lowerCamelCase_							:		Optional[int]							   =							{name: config.replace('ConfigMixin'						,   ''			) for name, config in model_name_to_config.items()}

	# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
	lowerCamelCase_							:		Tuple							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Any							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		int							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Optional[int]							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Dict							   =							collections.defaultdict(A			)

	# Let's lookup through all diffusers object (once).
	for attr_name in dir(A			):
		lowerCamelCase_							:		Tuple							   =							None
		if attr_name.endswith('Tokenizer'			):
			lowerCamelCase_							:		Union[str, Any]							   =							slow_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'			):
			lowerCamelCase_							:		Any							   =							fast_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-13]
		elif _re_tf_models.match(A			) is not None:
			lowerCamelCase_							:		Tuple							   =							tf_models
			lowerCamelCase_							:		Optional[int]							   =							_re_tf_models.match(A			).groups()[0]
		elif _re_flax_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							flax_models
			lowerCamelCase_							:		Union[str, Any]							   =							_re_flax_models.match(A			).groups()[0]
		elif _re_pt_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							pt_models
			lowerCamelCase_							:		Optional[int]							   =							_re_pt_models.match(A			).groups()[0]

		if lookup_dict is not None:
			while len(A			) > 0:
				if attr_name in model_name_to_prefix.values():
					lowerCamelCase_							:		List[Any]							   =							True
					break
				# Try again after removing the last word in the name
				lowerCamelCase_							:		int							   =							''.join(camel_case_split(A			)[:-1]			)

    # Let's build that table!
	lowerCamelCase_							:		Optional[int]							   =							list(model_name_to_config.keys()			)
	model_names.sort(key=str.lower			)
	lowerCamelCase_							:		str							   =							['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
	# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
	lowerCamelCase_							:		str							   =							[len(A			) + 2 for c in columns]
	lowerCamelCase_							:		Optional[int]							   =							max([len(A			) for name in model_names]			) + 2

	# Build the table per se
	lowerCamelCase_							:		Optional[Any]							   =							'|' + '|'.join([_center_text(A						,   A			) for c, w in zip(A						,   A			)]			) + '|\n'
	# Use ":-----:" format to center-aligned table cell texts
	table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]			) + "|\n"

	lowerCamelCase_							:		str							   =							{True: '✅', False: '❌'}
	for name in model_names:
		lowerCamelCase_							:		List[str]							   =							model_name_to_prefix[name]
		lowerCamelCase_							:		Tuple							   =							[
		    name,
		    check[slow_tokenizers[prefix]],
		    check[fast_tokenizers[prefix]],
		    check[pt_models[prefix]],
		    check[tf_models[prefix]],
		    check[flax_models[prefix]],
		]
		table += "|" + "|".join([_center_text(A						,   A			) for l, w in zip(A						,   A			)]			) + "|\n"
	return table






def 					snake_case					(							A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							_find_text_in_file(
	    filename=os.path.join(A						,   'index.mdx'			)						,   start_prompt='<!--This table is updated automatically from the auto modules'						,   end_prompt='<!-- End table-->'						,   )
	lowerCamelCase_							:		Any							   =							get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(A						,   'index.mdx'			)						,   'w'						,   encoding='utf-8'						,   newline='\n'			) as f:
				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]			)
		else:
			raise ValueError(
			    'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'			)


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

							check_model_table(args.fix_and_overwrite)
