



import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						set()
 # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
 for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  __SCREAMING_SNAKE_CASE							:     int							=						getattr(transformers.models.auto		,						__lowerCamelCase		,						__lowerCamelCase  )
  if module is None:
   continue
  # all mappings in a single auto modeling file
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						[
      x
      for x in dir(__lowerCamelCase  )
      if x.endswith("_MAPPING_NAMES"  )
      and (x.startswith("MODEL_"  ) or x.startswith("TF_MODEL_"  ) or x.startswith("FLAX_MODEL_"  ))
  ]
  for name in mapping_names:
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  )
   if mapping is not None:
    for v in mapping.values():
     if isinstance(__lowerCamelCase		,						(list, tuple)  ):
      model_names.update(__lowerCamelCase  )
     elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
      model_names.add(__lowerCamelCase  )

 return sorted(__lowerCamelCase  )






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						set(get_all_model_names()  )

 with open("tests/utils/tiny_model_summary.json"  ) as fp:
  __SCREAMING_SNAKE_CASE							:     Dict							=						json.load(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     int							=						set()
 for model_base_name in tiny_model_info:
  tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]  )

 # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model_names.difference(__lowerCamelCase  )
 for model_name in copy.copy(__lowerCamelCase  ):
  if not model_name.startswith("TF"  ) and F"""TF{model_name}""" in not_on_hub:
   tiny_models_names.remove(__lowerCamelCase  )
  elif model_name.startswith("TF"  ) and model_name[2:] in not_on_hub:
   tiny_models_names.remove(__lowerCamelCase  )

 return sorted(__lowerCamelCase  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						COMPOSITE_MODELS.values()

 # All tiny model base names on Hub
 __SCREAMING_SNAKE_CASE							:     Tuple							=						get_all_model_names()
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						hf_api.list_models(
     filter=ModelFilter(
         author="hf-internal-testing"		,						)  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						set()
 for x in models:
  __SCREAMING_SNAKE_CASE							:     Dict							=						x.modelId
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     int							=						model.split("/"  )
  if not model.startswith("tiny-random-"  ):
   continue
  __SCREAMING_SNAKE_CASE							:     Dict							=						model.replace("tiny-random-"		,						""  )
  if not model[0].isupper():
   continue
  if model not in model_names and model not in special_models:
   continue
  _models.add(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						sorted(_models  )
 # All tiny model names on Hub
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						{}
 for model in models:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						F"""hf-internal-testing/tiny-random-{model}"""
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						model.split("-"  )[0]
  try:
   __SCREAMING_SNAKE_CASE							:     List[str]							=						hf_api.repo_info(__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						{
       "tokenizer_classes": set(),
       "processor_classes": set(),
       "model_classes": set(),
       "sha": repo_info.sha,
   }
  except Exception:
   continue
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						AutoTokenizer.from_pretrained(__lowerCamelCase  )
   content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Any							=						AutoTokenizer.from_pretrained(__lowerCamelCase		,						use_fast=__lowerCamelCase  )
   content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     List[str]							=						AutoImageProcessor.from_pretrained(__lowerCamelCase  )
   content["processor_classes"].add(img_p.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						AutoFeatureExtractor.from_pretrained(__lowerCamelCase  )
   if not isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
    content["processor_classes"].add(feat_p.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE							:     int							=						model_class.from_pretrained(__lowerCamelCase  )
   content["model_classes"].add(m.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						getattr(__lowerCamelCase		,						F"""TF{model}"""  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						model_class.from_pretrained(__lowerCamelCase  )
   content["model_classes"].add(m.__class__.__name__  )
  except Exception:
   pass

  __SCREAMING_SNAKE_CASE							:     str							=						sorted(content["tokenizer_classes"]  )
  __SCREAMING_SNAKE_CASE							:     Tuple							=						sorted(content["processor_classes"]  )
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sorted(content["model_classes"]  )

  __SCREAMING_SNAKE_CASE							:     Any							=						content
  with open(os.path.join(__lowerCamelCase		,						"hub_tiny_model_summary.json"  )		,						"w"  ) as fp:
   json.dump(__lowerCamelCase		,						__lowerCamelCase		,						ensure_ascii=__lowerCamelCase		,						indent=4  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
      _lowerCamelCase         =	parser.parse_args()

      # This has to be `spawn` to avoid hanging forever!
      multiprocessing.set_start_method("""spawn""")

      _lowerCamelCase         =	"""tiny_models"""
      _lowerCamelCase         =	True
      _lowerCamelCase         =	None
      _lowerCamelCase         =	get_tiny_model_names_from_repo()
      _lowerCamelCase         =	True
      _lowerCamelCase         =	True
      _lowerCamelCase         =	"""hf-internal-testing"""

      create_tiny_models(
          output_path,
          all,
          model_types,
          models_to_skip,
          no_check,
          upload,
          organization,
          token=os.environ.get("""TOKEN""", None),
          num_workers=args.num_workers,
      )











from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__SCREAMING_SNAKE_CASE     =	{
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}




@pytest.mark.parametrize(
    "task_cls"			,     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,     )
def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    LanguageModeling(text_column="input_text"			)
									assert {"input_text": "text"} == task.column_mapping







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[int]:
									A				:       List[Any]														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Optional[int]:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[Any]														=    {"text_column": "input_text", "label_column": "input_labels"}
									A				:       Any														=    TextClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("text-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       Dict														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Any														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    QuestionAnsweringExtractive(
									    context_column="input_context"						,						question_column="input_question"						,						answers_column="input_answers"			)
									self.assertDictEqual(
									    {"input_context": "context", "input_question": "question", "input_answers": "answers"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    Summarization(text_column="input_text"						,						summary_column="input_summary"			)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    AutomaticSpeechRecognition(audio_column="input_audio"						,						transcription_column="input_transcription"			)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       Tuple														=    Features({"audio": Audio()}			)
									A				:       Any														=    Features({"transcription": Value("string"			)}			)
									A				:       List[str]														=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									A				:       Dict														=    AutomaticSpeechRecognition.from_dict(__lowerCamelCase			)
									self.assertEqual("automatic-speech-recognition"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, Any]:
									A				:       Optional[int]														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Union[str, Any]														=    Features({"audio": Audio()}			)
									A				:       Dict														=    Features({"labels": ClassLabel}			)
									A				:       Tuple														=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Any														=    AudioClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("audio-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[Any]														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    ImageClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("image-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[Any]														=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,						info=__lowerCamelCase			)
									assert isinstance(dataset.info.task_templates						,						__lowerCamelCase			)
									assert len(dataset.info.task_templates			) == 1

									def keep_task(__lowerCamelCase   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Tuple:
									A				:       Optional[Any]														=    Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
									A				:       Optional[Any]														=    TextClassification(text_column="text"						,						label_column="label"			)
									A				:       Optional[Any]														=    DatasetInfo(features=__lowerCamelCase						,						task_templates=__lowerCamelCase			)
									A				:       Optional[int]														=    Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}						,						info=__lowerCamelCase			)

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []