
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list







class 	_lowercase    (							unittest.TestCase					):


     '''simple docstring'''





     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Dict							=    "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "	)
          UpperCAmelCase__				:			Optional[int]							=    dict(zip(__UpperCamelCase			,      range(len(__UpperCamelCase	)	)	)	)

          UpperCAmelCase__				:			List[str]							=    {
              "pad_token": "<pad>",
              "unk_token": "<unk>",
              "bos_token": "<s>",
              "eos_token": "</s>",
          }
          UpperCAmelCase__				:			Union[str, Any]							=    {
              "feature_size": 1,
              "padding_value": 0.0,
              "sampling_rate": 1_60_00,
              "return_attention_mask": False,
              "do_normalize": True,
          }

          UpperCAmelCase__				:			List[Any]							=    tempfile.mkdtemp()
          UpperCAmelCase__				:			Optional[Any]							=    os.path.join(self.tmpdirname			,      VOCAB_FILES_NAMES["vocab_file"]	)
          UpperCAmelCase__				:			Tuple							=    os.path.join(self.tmpdirname			,      __UpperCamelCase	)
          with open(self.vocab_file			,      "w"			,      encoding="utf-8"	) as fp:
               fp.write(json.dumps(__UpperCamelCase	) + "\n"	)

          with open(self.feature_extraction_file			,      "w"			,      encoding="utf-8"	) as fp:
               fp.write(json.dumps(__UpperCamelCase	) + "\n"	)



     def 			lowerCAmelCase__							(   self			,      **__UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			str							=    self.add_kwargs_tokens_map.copy()
          kwargs.update(__UpperCamelCase	)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,      **__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      **__UpperCamelCase	)->       Optional[int]:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,      **__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          shutil.rmtree(self.tmpdirname	)



     def 			lowerCAmelCase__							(   self	)->       Dict:
          UpperCAmelCase__				:			Union[str, Any]							=    self.get_tokenizer()
          UpperCAmelCase__				:			Dict							=    self.get_feature_extractor()

          UpperCAmelCase__				:			List[Any]							=    WavaVecaProcessor(tokenizer=__UpperCamelCase			,      feature_extractor=__UpperCamelCase	)

          processor.save_pretrained(self.tmpdirname	)
          UpperCAmelCase__				:			Tuple							=    WavaVecaProcessor.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	)->       List[str]:
          UpperCAmelCase__				:			Optional[Any]							=    WavaVecaProcessor(tokenizer=self.get_tokenizer()			,      feature_extractor=self.get_feature_extractor()	)
          processor.save_pretrained(self.tmpdirname	)

          UpperCAmelCase__				:			Optional[Any]							=    self.get_tokenizer(bos_token="(BOS)"			,      eos_token="(EOS)"	)
          UpperCAmelCase__				:			Any							=    self.get_feature_extractor(do_normalize=__UpperCamelCase			,      padding_value=1.0	)

          UpperCAmelCase__				:			List[Any]							=    WavaVecaProcessor.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	)->       Any:
          UpperCAmelCase__				:			int							=    self.get_feature_extractor()
          UpperCAmelCase__				:			Dict							=    self.get_tokenizer()

          UpperCAmelCase__				:			Tuple							=    WavaVecaProcessor(tokenizer=__UpperCamelCase			,      feature_extractor=__UpperCamelCase	)

          UpperCAmelCase__				:			str							=    floats_list((3, 10_00)	)

          UpperCAmelCase__				:			int							=    feature_extractor(__UpperCamelCase			,      return_tensors="np"	)
          UpperCAmelCase__				:			Optional[Any]							=    processor(__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]:
          UpperCAmelCase__				:			Optional[int]							=    self.get_feature_extractor()
          UpperCAmelCase__				:			List[str]							=    self.get_tokenizer()

          UpperCAmelCase__				:			Union[str, Any]							=    WavaVecaProcessor(tokenizer=__UpperCamelCase			,      feature_extractor=__UpperCamelCase	)

          UpperCAmelCase__				:			Union[str, Any]							=    "This is a test string"

          UpperCAmelCase__				:			str							=    processor(text=__UpperCamelCase	)

          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer(__UpperCamelCase	)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,      encoded_processor[key]	)



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[Any]							=    self.get_feature_extractor()
          UpperCAmelCase__				:			Tuple							=    self.get_tokenizer()

          UpperCAmelCase__				:			List[Any]							=    WavaVecaProcessor(tokenizer=__UpperCamelCase			,      feature_extractor=__UpperCamelCase	)

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

          UpperCAmelCase__				:			Optional[Any]							=    processor.batch_decode(__UpperCamelCase	)
          UpperCAmelCase__				:			Tuple							=    tokenizer.batch_decode(__UpperCamelCase	)

          self.assertListEqual(__UpperCamelCase			,      __UpperCamelCase	)



     def 			lowerCAmelCase__							(   self	)->       List[str]:
          UpperCAmelCase__				:			Optional[int]							=    self.get_feature_extractor()
          UpperCAmelCase__				:			Any							=    self.get_tokenizer()

          UpperCAmelCase__				:			int							=    WavaVecaProcessor(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"			,      )





"""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__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase							,     "r"							,     encoding="utf-8"							,     newline="\n"		) as f:
          UpperCAmelCase__				:			List[str]							=    f.readlines()
     # Find the start prompt.
     UpperCAmelCase__				:			Union[str, Any]							=    0
     while not lines[start_index].startswith(lowerCAmelCase		):
          start_index += 1
     start_index += 1

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def 				a__			(					lowerCAmelCase						:							List[str]		):






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



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			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.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

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

          if lookup_dict is not None:
               while len(lowerCAmelCase		) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__				:			Optional[Any]							=    True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__				:			Union[str, Any]							=    "".join(camel_case_split(lowerCAmelCase		)[:-1]		)

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

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "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__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    A__   :						str 							=     parser.parse_args()

    check_model_table(args.fix_and_overwrite)



