
"""simple docstring"""




import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


A__   :						int 							=     get_tests_dir("""fixtures/spiece.model""")







@require_sentencepiece
@require_tokenizers
class 	_lowercase    (							lowerCAmelCase_						, unittest.TestCase					):


     '''simple docstring'''





     _A		    =				ErnieMTokenizer
     _A		    =				False
     _A		    =				True
     _A		    =				False
     _A		    =				False



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__				:			int							=    ErnieMTokenizer(__UpperCamelCase			,      unk_token="<unk>"			,      pad_token="<pad>"	)
          tokenizer.save_pretrained(self.tmpdirname	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       List[Any]:
          UpperCAmelCase__				:			Tuple							=    "this is a test"
          UpperCAmelCase__				:			Union[str, Any]							=    "this is a test"
          return input_text, output_text



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			List[Any]							=    "<pad>"
          UpperCAmelCase__				:			Union[str, Any]							=    0

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



     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			Optional[Any]							=    list(self.get_tokenizer().get_vocab().keys()	)

          self.assertEqual(vocab_keys[0]			,      "<pad>"	)
          self.assertEqual(vocab_keys[1]			,      "<unk>"	)
          self.assertEqual(vocab_keys[-1]			,      "▁eloquent"	)
          self.assertEqual(len(__UpperCamelCase	)			,      3_00_00	)



     def 			lowerCAmelCase__							(   self	)->       Dict:
          self.assertEqual(self.get_tokenizer().vocab_size			,      3_00_00	)



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          if not self.test_rust_tokenizer:
               return

          UpperCAmelCase__				:			Any							=    self.get_tokenizer()
          UpperCAmelCase__				:			List[str]							=    self.get_rust_tokenizer()

          UpperCAmelCase__				:			List[Any]							=    "I was born in 92000, and this is falsé."

          UpperCAmelCase__				:			Any							=    tokenizer.tokenize(__UpperCamelCase	)
          UpperCAmelCase__				:			Tuple							=    rust_tokenizer.tokenize(__UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      __UpperCamelCase	)

          UpperCAmelCase__				:			Dict							=    tokenizer.encode(__UpperCamelCase			,      add_special_tokens=__UpperCamelCase	)
          UpperCAmelCase__				:			Dict							=    rust_tokenizer.encode(__UpperCamelCase			,      add_special_tokens=__UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      __UpperCamelCase	)

          UpperCAmelCase__				:			List[Any]							=    self.get_rust_tokenizer()
          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer.encode(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[Any]							=    rust_tokenizer.encode(__UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      __UpperCamelCase	)



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Dict							=    ErnieMTokenizer(__UpperCamelCase			,      do_lower_case=__UpperCamelCase			,      unk_token="<unk>"			,      pad_token="<pad>"	)

          UpperCAmelCase__				:			str							=    tokenizer.tokenize("This is a test"	)
          self.assertListEqual(__UpperCamelCase			,      ["▁this", "▁is", "▁a", "▁test"]	)

          self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase	)			,      [48, 25, 21, 12_89]	)

          UpperCAmelCase__				:			int							=    tokenizer.tokenize("I was born in 92000, and this is falsé."	)
          # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
          self.assertListEqual(
              __UpperCamelCase			,      ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
          UpperCAmelCase__				:			List[str]							=    tokenizer.convert_tokens_to_ids(__UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      [31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]	)

          UpperCAmelCase__				:			List[Any]							=    tokenizer.convert_ids_to_tokens(__UpperCamelCase	)
          self.assertListEqual(
              __UpperCamelCase			,      ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,      )



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          UpperCAmelCase__				:			Tuple							=    ErnieMTokenizer(__UpperCamelCase			,      unk_token="<unk>"			,      pad_token="<pad>"	)

          UpperCAmelCase__				:			List[str]							=    tokenizer.encode("sequence builders"	)
          UpperCAmelCase__				:			str							=    tokenizer.encode("multi-sequence build"	)

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

          assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
          assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
              tokenizer.sep_token_id
          ] + text_a + [tokenizer.sep_token_id]



     @slow
     def 			lowerCAmelCase__							(   self	)->       Any:
          # fmt: off
          UpperCAmelCase__				:			List[str]							=    {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=__UpperCamelCase			,      model_name="susnato/ernie-m-base_pytorch"			,      sequences=[
                  "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
                  "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
                  "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
                  "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
                  "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
                  "conditioning on both left and right context in all layers.",
                  "The quick brown fox jumps over the lazy dog.",
              ]			,      )





"""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)



