import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


A   :     int       =				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def    a__       (						__UpperCamelCase ):
      if "token" in model_name_or_path:
            return "rag_token"
      if "sequence" in model_name_or_path:
            return "rag_sequence"
      if "bart" in model_name_or_path:
            return "bart"
      return None


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      return max(metric_fn(__UpperCamelCase   ,					__UpperCamelCase ) for gt in ground_truths )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     [line.strip() for line in open(__UpperCamelCase   ,					"r" ).readlines()]
      SCREAMING_SNAKE_CASE_      =     []

      if args.gold_data_mode == "qa":
            SCREAMING_SNAKE_CASE_      =     pd.read_csv(__UpperCamelCase   ,					sep="\t"   ,					header=__UpperCamelCase )
            for answer_list in data[1]:
                  SCREAMING_SNAKE_CASE_      =     ast.literal_eval(__UpperCamelCase )
                  answers.append(__UpperCamelCase )
      else:
            SCREAMING_SNAKE_CASE_      =     [line.strip() for line in open(__UpperCamelCase   ,					"r" ).readlines()]
            SCREAMING_SNAKE_CASE_      =     [[reference] for reference in references]

      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     0
      for prediction, ground_truths in zip(__UpperCamelCase   ,					__UpperCamelCase ):
            total += 1
            em += metric_max_over_ground_truths(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
            fa += metric_max_over_ground_truths(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     100.0 * em / total
      SCREAMING_SNAKE_CASE_      =     100.0 * fa / total

      logger.info(F'''F1: {fa:.2f}''' )
      logger.info(F'''EM: {em:.2f}''' )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     args.k
      SCREAMING_SNAKE_CASE_      =     [line.strip() for line in open(__UpperCamelCase   ,					"r" ).readlines()]
      SCREAMING_SNAKE_CASE_      =     [line.strip() for line in open(__UpperCamelCase   ,					"r" ).readlines()]

      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     0
      for hypo, reference in zip(__UpperCamelCase   ,					__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     set(hypo.split("\t" )[:k] )
            SCREAMING_SNAKE_CASE_      =     set(reference.split("\t" ) )
            total += 1
            em += len(hypo_provenance & ref_provenance ) / k

      SCREAMING_SNAKE_CASE_      =     100.0 * em / total
      logger.info(F'''Precision@{k}: {em: .2f}''' )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      def strip_title(__UpperCamelCase ):
            if title.startswith("\"" ):
                  SCREAMING_SNAKE_CASE_      =     title[1:]
            if title.endswith("\"" ):
                  SCREAMING_SNAKE_CASE_      =     title[:-1]
            return title

      SCREAMING_SNAKE_CASE_      =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __UpperCamelCase   ,					return_tensors="pt"   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					)["input_ids"].to(args.device )

      SCREAMING_SNAKE_CASE_      =     rag_model.rag.question_encoder(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     question_enc_outputs[0]

      SCREAMING_SNAKE_CASE_      =     rag_model.retriever(
          __UpperCamelCase   ,					question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy()   ,					prefix=rag_model.rag.generator.config.prefix   ,					n_docs=rag_model.config.n_docs   ,					return_tensors="pt"   ,					)
      SCREAMING_SNAKE_CASE_      =     rag_model.retriever.index.get_doc_dicts(result.doc_ids )
      SCREAMING_SNAKE_CASE_      =     []
      for docs in all_docs:
            SCREAMING_SNAKE_CASE_      =     [strip_title(__UpperCamelCase ) for title in docs["title"]]
            provenance_strings.append("\t".join(__UpperCamelCase ) )
      return provenance_strings


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_      =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                __UpperCamelCase   ,					return_tensors="pt"   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase )

            SCREAMING_SNAKE_CASE_      =     inputs_dict.input_ids.to(args.device )
            SCREAMING_SNAKE_CASE_      =     inputs_dict.attention_mask.to(args.device )
            SCREAMING_SNAKE_CASE_      =     rag_model.generate(  # rag_model overwrites generate
                __UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					num_beams=args.num_beams   ,					min_length=args.min_length   ,					max_length=args.max_length   ,					early_stopping=__UpperCamelCase   ,					num_return_sequences=1   ,					bad_words_ids=[[0, 0]]   ,					)
            SCREAMING_SNAKE_CASE_      =     rag_model.retriever.generator_tokenizer.batch_decode(__UpperCamelCase   ,					skip_special_tokens=__UpperCamelCase )

            if args.print_predictions:
                  for q, a in zip(__UpperCamelCase   ,					__UpperCamelCase ):
                        logger.info("Q: {} - A: {}".format(__UpperCamelCase   ,					__UpperCamelCase ) )

            return answers


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser()
      parser.add_argument(
          "--model_type"   ,					choices=["rag_sequence", "rag_token", "bart"]   ,					type=__UpperCamelCase   ,					help=(
              "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
              " model_name_or_path"
          )   ,					)
      parser.add_argument(
          "--index_name"   ,					default=__UpperCamelCase   ,					choices=["exact", "compressed", "legacy"]   ,					type=__UpperCamelCase   ,					help="RAG model retriever type"   ,					)
      parser.add_argument(
          "--index_path"   ,					default=__UpperCamelCase   ,					type=__UpperCamelCase   ,					help="Path to the retrieval index"   ,					)
      parser.add_argument("--n_docs"   ,					default=5   ,					type=__UpperCamelCase   ,					help="Number of retrieved docs" )
      parser.add_argument(
          "--model_name_or_path"   ,					default=__UpperCamelCase   ,					type=__UpperCamelCase   ,					required=__UpperCamelCase   ,					help="Path to pretrained checkpoints or model identifier from huggingface.co/models"   ,					)
      parser.add_argument(
          "--eval_mode"   ,					choices=["e2e", "retrieval"]   ,					default="e2e"   ,					type=__UpperCamelCase   ,					help=(
              "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
              " precision@k."
          )   ,					)
      parser.add_argument("--k"   ,					default=1   ,					type=__UpperCamelCase   ,					help="k for the precision@k calculation" )
      parser.add_argument(
          "--evaluation_set"   ,					default=__UpperCamelCase   ,					type=__UpperCamelCase   ,					required=__UpperCamelCase   ,					help="Path to a file containing evaluation samples"   ,					)
      parser.add_argument(
          "--gold_data_path"   ,					default=__UpperCamelCase   ,					type=__UpperCamelCase   ,					required=__UpperCamelCase   ,					help="Path to a tab-separated file with gold samples"   ,					)
      parser.add_argument(
          "--gold_data_mode"   ,					default="qa"   ,					type=__UpperCamelCase   ,					choices=["qa", "ans"]   ,					help=(
              "Format of the gold data file"
              "qa - a single line in the following format: question [tab] answer_list"
              "ans - a single line of the gold file contains the expected answer string"
          )   ,					)
      parser.add_argument(
          "--predictions_path"   ,					type=__UpperCamelCase   ,					default="predictions.txt"   ,					help="Name of the predictions file, to be stored in the checkpoints directory"   ,					)
      parser.add_argument(
          "--eval_all_checkpoints"   ,					action="store_true"   ,					help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"   ,					)
      parser.add_argument(
          "--eval_batch_size"   ,					default=8   ,					type=__UpperCamelCase   ,					help="Batch size per GPU/CPU for evaluation."   ,					)
      parser.add_argument(
          "--recalculate"   ,					help="Recalculate predictions even if the prediction file exists"   ,					action="store_true"   ,					)
      parser.add_argument(
          "--num_beams"   ,					default=4   ,					type=__UpperCamelCase   ,					help="Number of beams to be used when generating answers"   ,					)
      parser.add_argument("--min_length"   ,					default=1   ,					type=__UpperCamelCase   ,					help="Min length of the generated answers" )
      parser.add_argument("--max_length"   ,					default=5_0   ,					type=__UpperCamelCase   ,					help="Max length of the generated answers" )

      parser.add_argument(
          "--print_predictions"   ,					action="store_true"   ,					help="If True, prints predictions while evaluating."   ,					)
      parser.add_argument(
          "--print_docs"   ,					action="store_true"   ,					help="If True, prints docs retried while generating."   ,					)
      SCREAMING_SNAKE_CASE_      =     parser.parse_args()
      SCREAMING_SNAKE_CASE_      =     torch.device("cuda" if torch.cuda.is_available() else "cpu" )
      return args


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     {}
      if args.model_type is None:
            SCREAMING_SNAKE_CASE_      =     infer_model_type(args.model_name_or_path )
            assert args.model_type is not None
      if args.model_type.startswith("rag" ):
            SCREAMING_SNAKE_CASE_      =     RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
            SCREAMING_SNAKE_CASE_      =     args.n_docs
            if args.index_name is not None:
                  SCREAMING_SNAKE_CASE_      =     args.index_name
            if args.index_path is not None:
                  SCREAMING_SNAKE_CASE_      =     args.index_path
      else:
            SCREAMING_SNAKE_CASE_      =     BartForConditionalGeneration

      SCREAMING_SNAKE_CASE_      =     (
          [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
          if args.eval_all_checkpoints
          else [args.model_name_or_path]
      )

      logger.info("Evaluate the following checkpoints: %s"   ,					__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     get_scores if args.eval_mode == "e2e" else get_precision_at_k
      SCREAMING_SNAKE_CASE_      =     evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

      for checkpoint in checkpoints:
            if os.path.exists(args.predictions_path ) and (not args.recalculate):
                  logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
                  score_fn(__UpperCamelCase   ,					args.predictions_path   ,					args.gold_data_path )
                  continue

            logger.info("***** Running evaluation for {} *****".format(__UpperCamelCase ) )
            logger.info("  Batch size = %d"   ,					args.eval_batch_size )
            logger.info("  Predictions will be stored under {}".format(args.predictions_path ) )

            if args.model_type.startswith("rag" ):
                  SCREAMING_SNAKE_CASE_      =     RagRetriever.from_pretrained(__UpperCamelCase   ,					**__UpperCamelCase )
                  SCREAMING_SNAKE_CASE_      =     model_class.from_pretrained(__UpperCamelCase   ,					retriever=__UpperCamelCase   ,					**__UpperCamelCase )
                  model.retriever.init_retrieval()
            else:
                  SCREAMING_SNAKE_CASE_      =     model_class.from_pretrained(__UpperCamelCase   ,					**__UpperCamelCase )
            model.to(args.device )

            with open(args.evaluation_set   ,					"r" ) as eval_file, open(args.predictions_path   ,					"w" ) as preds_file:
                  SCREAMING_SNAKE_CASE_      =     []
                  for line in tqdm(__UpperCamelCase ):
                        questions.append(line.strip() )
                        if len(__UpperCamelCase ) == args.eval_batch_size:
                              SCREAMING_SNAKE_CASE_      =     evaluate_batch_fn(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
                              preds_file.write("\n".join(__UpperCamelCase ) + "\n" )
                              preds_file.flush()
                              SCREAMING_SNAKE_CASE_      =     []
                  if len(__UpperCamelCase ) > 0:
                        SCREAMING_SNAKE_CASE_      =     evaluate_batch_fn(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
                        preds_file.write("\n".join(__UpperCamelCase ) )
                        preds_file.flush()

                  score_fn(__UpperCamelCase   ,					args.predictions_path   ,					args.gold_data_path )


if __name__ == "__main__":
      A   :     Optional[int]       =				get_args()
      main(args)



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   :     Tuple       =				"src/transformers"
A   :     Optional[Any]       =				"docs/source/en/tasks"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"r"   ,					encoding="utf-8"   ,					newline="\n" ) as f:
            SCREAMING_SNAKE_CASE_      =     f.readlines()
      # Find the start prompt.
      SCREAMING_SNAKE_CASE_      =     0
      while not lines[start_index].startswith(__UpperCamelCase ):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE_      =     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[str]       =				direct_transformers_import(TRANSFORMERS_PATH)

A   :     List[Any]       =				{
    "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   :     Any       =				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase   ,					set() )
      SCREAMING_SNAKE_CASE_      =     {
          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    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     _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-->"   ,					)

      SCREAMING_SNAKE_CASE_      =     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   :     Tuple       =				argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      A   :     Dict       =				parser.parse_args()

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


