import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

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/update_metadata.py
SCREAMING_SNAKE_CASE :	List[str]							     =		"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE :	int							     =		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE :	List[str]							     =		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.
SCREAMING_SNAKE_CASE :	int							     =		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def   __A		(			_A					):



 """simple docstring"""


 __a       =  re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,				_A					)
 return [m.group(0					) for m in matches]



def   __A		(			):



 """simple docstring"""


 __a       =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 __a       =  {
     config.replace("Config"							,				""					): model_type for model_type, config in config_maping_names.items()
 }

 # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
 __a       =  collections.defaultdict(_A					)
 __a       =  collections.defaultdict(_A					)
 __a       =  collections.defaultdict(_A					)

 # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
 for attr_name in dir(_A					):
  __a       =  None
  if _re_tf_models.match(_A					) is not None:
   __a       =  tf_models
   __a       =  _re_tf_models.match(_A					).groups()[0]
  elif _re_flax_models.match(_A					) is not None:
   __a       =  flax_models
   __a       =  _re_flax_models.match(_A					).groups()[0]
  elif _re_pt_models.match(_A					) is not None:
   __a       =  pt_models
   __a       =  _re_pt_models.match(_A					).groups()[0]

  if lookup_dict is not None:
   while len(_A					) > 0:
    if attr_name in model_prefix_to_model_type:
     __a       =  True
     break
    # Try again after removing the last word in the name
    __a       =  "".join(camel_case_split(_A					)[:-1]					)

 __a       =  set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
 __a       =  list(_A					)
 all_models.sort()

 __a       =  {"model_type": all_models}
 __a       =  [pt_models[t] for t in all_models]
 __a       =  [tf_models[t] for t in all_models]
 __a       =  [flax_models[t] for t in all_models]

 # Now let's use the auto-mapping names to make sure
 __a       =  {}
 for t in all_models:
  if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
   __a       =  "AutoProcessor"
  elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
   __a       =  "AutoTokenizer"
  elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
   __a       =  "AutoFeatureExtractor"
  else:
   # Default to AutoTokenizer if a model has nothing, for backward compatibility.
   __a       =  "AutoTokenizer"

 __a       =  [processors[t] for t in all_models]

 return pd.DataFrame(_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [
     transformers_module.models.auto.modeling_auto,
     transformers_module.models.auto.modeling_tf_auto,
     transformers_module.models.auto.modeling_flax_auto,
 ]
 for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
  __a       =  [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
  __a       =  [auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
  # Loop through all three frameworks
  for module, cls, mapping in zip(_A							,				_A							,				_A					):
   # The type of pipeline may not exist in this framework
   if not hasattr(_A							,				_A					):
    continue
   # First extract all model_names
   __a       =  []
   for name in getattr(_A							,				_A					).values():
    if isinstance(_A							,				_A					):
     model_names.append(_A					)
    else:
     model_names.extend(list(_A					)					)

            # Add pipeline tag and auto model class for those models
   table.update({model_name: (pipeline_tag, cls) for model_name in model_names}					)

 return table



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_frameworks_table()
 __a       =  Dataset.from_pandas(_A					)

 __a       =  hf_hub_download(
     "huggingface/transformers-metadata"							,				"pipeline_tags.json"							,				repo_type="dataset"							,				token=_A					)
 __a       =  Dataset.from_json(_A					)
 __a       =  {
     tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
     for i in range(len(_A					)					)
 }
 __a       =  update_pipeline_and_auto_class_table(_A					)

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 __a       =  sorted(table.keys()					)
 __a       =  pd.DataFrame(
     {
         "model_class": model_classes,
         "pipeline_tag": [table[m][0] for m in model_classes],
         "auto_class": [table[m][1] for m in model_classes],
     }					)
 __a       =  Dataset.from_pandas(_A					)

 with tempfile.TemporaryDirectory() as tmp_dir:
  frameworks_dataset.to_json(os.path.join(_A							,				"frameworks.json"					)					)
  tags_dataset.to_json(os.path.join(_A							,				"pipeline_tags.json"					)					)

  if commit_sha is not None:
   __a       =  (
       f"""Update with commit {commit_sha}\n\nSee: """
       f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
   )
  else:
   __a       =  "Update"

  upload_folder(
      repo_id="huggingface/transformers-metadata"							,				folder_path=_A							,				repo_type="dataset"							,				token=_A							,				commit_message=_A							,				)



def   __A		(			):



 """simple docstring"""


 __a       =  {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 __a       =  transformers_module.pipelines.SUPPORTED_TASKS
 __a       =  []
 for key in pipeline_tasks:
  if key not in in_table:
   __a       =  pipeline_tasks[key]["pt"]
   if isinstance(_A							,				(list, tuple)					):
    __a       =  model[0]
   __a       =  model.__name__
   if model not in in_table.values():
    missing.append(_A					)

 if len(_A					) > 0:
  __a       =  ", ".join(_A					)
  raise ValueError(
      "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
      f"""`utils/update_metadata.py`: {msg}. Please add them!"""					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	List[Any]							     =		argparse.ArgumentParser()
  parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
  parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
  parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
  SCREAMING_SNAKE_CASE :	int							     =		parser.parse_args()

  if args.check_only:
    check_pipeline_tags()
  else:
    update_metadata(args.token, args.commit_sha)



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

 with open(f"""{dataset_id}_eval_results.txt"""							,				"w"					) as f:
  f.write(_A					)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  f"""log_{dataset_id}_targets.txt"""

  with open(_A							,				"w"					) as p, open(_A							,				"w"					) as t:
   # mapping function to write output
   def write_to_file(_A							,				_A					):
    p.write(f"""{i}""" + "\n"					)
    p.write(batch["prediction"] + "\n"					)
    t.write(f"""{i}""" + "\n"					)
    t.write(batch["target"] + "\n"					)

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

 # compute and log_results
 # do not change function below
 log_results(_A							,				_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Any							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


