


'''simple docstring'''



import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A__						:   Any				=    """src/transformers"""
A__						:   Union[str, Any]				=    """docs/source/en/tasks"""



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Optional[int]				)	->						List[str]:
     with open(UpperCAmelCase_ ,       'r' ,       encoding='utf-8' ,       newline='\n'				) as f:
          __lowerCamelCase  :      Optional[int]														=						f.readlines()
     # Find the start prompt.
     __lowerCamelCase  :      str														=						0
     while not lines[start_index].startswith(UpperCAmelCase_				):
          start_index += 1
     start_index += 1

     __lowerCamelCase  :      List[str]														=						start_index
     while not lines[end_index].startswith(UpperCAmelCase_				):
          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


# This is to make sure the transformers module imported is the one in the repo.
A__						:   List[Any]				=    direct_transformers_import(TRANSFORMERS_PATH)

A__						:   int				=    {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A__						:   List[Any]				=    {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						List[str]:
     __lowerCamelCase  :      Dict														=						TASK_GUIDE_TO_MODELS[task_guide]
     __lowerCamelCase  :      Tuple														=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_ ,       set()				)
     __lowerCamelCase  :      str														=						{
         code: name
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if (code in model_maping_names or code in special_model_types)
     }
     return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Dict=False				)	->						Any:
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						_find_text_in_file(
         filename=os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				) ,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->' ,       end_prompt='<!--End of the generated tip-->' ,       )

     __lowerCamelCase  :      Optional[Any]														=						get_model_list_for_task(UpperCAmelCase_				)

     if current_list != new_list:
          if overwrite:
               with open(os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				) ,       'w' ,       encoding='utf-8' ,       newline='\n'				) as f:
                    f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
          else:
               raise ValueError(
                   F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                   ' to fix this.'				)


if __name__ == "__main__":
      A__						:   Any				=    argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      A__						:   Optional[Any]				=    parser.parse_args()

      for task_guide in TASK_GUIDE_TO_MODELS.keys():
            check_model_list_for_task(task_guide, args.fix_and_overwrite)










"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__					=get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__					={"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__					=">>zh<<"
UpperCAmelCase__					="Helsinki-NLP/"

if is_torch_available():
       UpperCAmelCase__					="pt"
elif is_tf_available():
       UpperCAmelCase__					="tf"
else:
       UpperCAmelCase__					="jax"





@require_sentencepiece
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Dict           =		MarianTokenizer
      a			:  Optional[int]           =		False
      a			:  Any           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       super().setUp()
       __lowercase						=						["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase						=						dict(zip(A_   ,							range(len(A_				)				)				)				)
       __lowercase						=						Path(self.tmpdirname				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

       __lowercase						=						MarianTokenizer.from_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							**A_						:						Any				):






       '''simple docstring'''


       return MarianTokenizer.from_pretrained(self.tmpdirname   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						"""</s>"""
       __lowercase						=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""</s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<unk>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<pad>"""				)
       self.assertEqual(len(A_				)   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''				)
       __lowercase						=						en_de_tokenizer(["""I am a small frog"""]   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       __lowercase						=						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(A_   ,							batch.input_ids[0]				)

       __lowercase						=						tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_				)
       __lowercase						=						[x.name for x in Path(A_				).glob("""*"""				)]
       self.assertIn("""source.spm"""   ,							A_				)
       MarianTokenizer.from_pretrained(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()

       __lowercase						=						tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]   ,							padding=A_   ,							truncation=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch.input_ids.shape   ,							(2, 5_1_2)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()
       __lowercase						=						tok(["""I am a tiny frog""", """I am a small frog"""]   ,							padding=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch_smaller.input_ids.shape   ,							(2, 1_0)				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_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, 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, 1, 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], [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, 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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""Helsinki-NLP/opus-mt-en-de"""   ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,							decode_kwargs={"""use_source_tokenizer""": True}   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

       __lowercase						=						"""Tämä on testi"""
       __lowercase						=						"""This is a test"""

       __lowercase						=						[7_6, 7, 2_0_4_7, 2]
       __lowercase						=						[6_9, 1_2, 1_1, 9_4_0, 2]

       __lowercase						=						tokenizer(A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer(text_target=A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.decode(A_   ,							skip_special_tokens=A_				)
       self.assertEqual(A_   ,							A_				)
