




"""simple docstring"""




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
a_            =   'src/diffusers'
a_            =   'docs/source/en'
a_            =   '.'







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

			__lowercase	:       List[str]				=   start_index
			while not lines[end_index].startswith(lowercase__					):
						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 |
a_            =   'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a_            =   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a_            =   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.
a_            =   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       Optional[Any]				=   re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''					,							lowercase__					)
			return [m.group(0					) for m in matches]







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			__lowercase	:       List[Any]				=   2 if text == "✅" or text == "❌" else len(lowercase__					)
			__lowercase	:       List[Any]				=   (width - text_length) // 2
			__lowercase	:       Tuple				=   width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent







def       __UpperCAmelCase      (				):
			__lowercase	:       int				=   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__lowercase	:       Dict				=   {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			__lowercase	:       Tuple				=   {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.
			__lowercase	:       Dict				=   collections.defaultdict(lowercase__					)
			__lowercase	:       List[Any]				=   collections.defaultdict(lowercase__					)
			__lowercase	:       List[str]				=   collections.defaultdict(lowercase__					)
			__lowercase	:       int				=   collections.defaultdict(lowercase__					)
			__lowercase	:       Optional[Any]				=   collections.defaultdict(lowercase__					)

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

						if lookup_dict is not None:
									while len(lowercase__					) > 0:
												if attr_name in model_name_to_prefix.values():
															__lowercase	:       Tuple				=   True
															break
												# Try again after removing the last word in the name
												__lowercase	:       Dict				=   "".join(camel_case_split(lowercase__					)[:-1]					)

    # Let's build that table!
			__lowercase	:       Any				=   list(model_name_to_config.keys()					)
			model_names.sort(key=str.lower					)
			__lowercase	:       Union[str, Any]				=   ["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).
			__lowercase	:       Union[str, Any]				=   [len(lowercase__					) + 2 for c in columns]
			__lowercase	:       int				=   max([len(lowercase__					) for name in model_names]					) + 2

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

			__lowercase	:       Tuple				=   {True: "✅", False: "❌"}
			for name in model_names:
						__lowercase	:       Tuple				=   model_name_to_prefix[name]
						__lowercase	:       Union[str, Any]				=   [
						    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(lowercase__					,							lowercase__					) for l, w in zip(lowercase__					,							lowercase__					)]					) + "|\n"
			return table







def       __UpperCAmelCase      (				__UpperCamelCase=False					):
			__lowercase	:       Optional[int]				=   _find_text_in_file(
			    filename=os.path.join(lowercase__					,							'''index.mdx'''					)					,							start_prompt='''<!--This table is updated automatically from the auto modules'''					,							end_prompt='''<!-- End table-->'''					,							)
			__lowercase	:       Union[str, Any]				=   get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(lowercase__					,							'''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__":
			a_            =   argparse.ArgumentParser()
			parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
			a_            =   parser.parse_args()

			check_model_table(args.fix_and_overwrite)









"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
			from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

			from .test_feature_extraction_speechta import floats_list


a_            =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   tempfile.mkdtemp()

					__lowercase	:       List[str]				=   SpeechTaTokenizer(UpperCamelCase_   )
					tokenizer.save_pretrained(self.tmpdirname   )

					__lowercase	:       Dict				=   {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__lowercase	:       List[Any]				=   os.path.join(self.tmpdirname							,		UpperCamelCase_   )
					with open(self.feature_extraction_file							,		'''w'''							,		encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(UpperCamelCase_   ) + '''\n'''   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[Any]:
					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[str]				=   self.get_tokenizer()
					__lowercase	:       int				=   self.get_feature_extractor()

					__lowercase	:       Any				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					processor.save_pretrained(self.tmpdirname   )
					__lowercase	:       List[Any]				=   SpeechTaProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()							,		feature_extractor=self.get_feature_extractor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       str				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Dict				=   self.get_feature_extractor(do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					__lowercase	:       Tuple				=   SpeechTaProcessor.from_pretrained(
					    self.tmpdirname							,		bos_token='''(BOS)'''							,		eos_token='''(EOS)'''							,		do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Optional[Any]				=   self.get_feature_extractor()
					__lowercase	:       Optional[Any]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       int				=   floats_list((3, 10_00)   )

					__lowercase	:       str				=   feature_extractor(audio=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       Dict				=   processor(audio=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       str				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   floats_list((3, 10_00)   )

					__lowercase	:       List[str]				=   feature_extractor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       List[str]				=   processor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       List[Any]				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       List[str]				=   '''This is a test string'''

					__lowercase	:       Any				=   processor(text=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       List[Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   '''This is a test string'''

					__lowercase	:       Tuple				=   processor(text_target=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       int				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__lowercase	:       str				=   processor.batch_decode(UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.batch_decode(UpperCamelCase_   )

					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.get_feature_extractor()
					__lowercase	:       int				=   self.get_tokenizer()

					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					self.assertListEqual(
					    processor.model_input_names							,		feature_extractor.model_input_names							,		msg='''`processor` and `feature_extractor` model input names do not match'''							,		)



