






"""simple docstring"""
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


lowerCamelCase_							      = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def 							snake_case (  A__		):




			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 							snake_case (  A__      ,A__      ,A__		):
			return max(metric_fn(A__      ,A__		) for gt in ground_truths		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   [line.strip() for line in open(A__      ,"r"		).readlines()]
			UpperCAmelCase_	:					Optional[int]     	=   []

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

			UpperCAmelCase_	:					Optional[Any]     	=   0
			for prediction, ground_truths in zip(A__      ,A__		):
						total += 1
						em += metric_max_over_ground_truths(A__      ,A__      ,A__		)
						fa += metric_max_over_ground_truths(A__      ,A__      ,A__		)

			UpperCAmelCase_	:					Dict     	=   100.0 * em / total
			UpperCAmelCase_	:					int     	=   100.0 * fa / total

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

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Any     	=   args.k
			UpperCAmelCase_	:					str     	=   [line.strip() for line in open(A__      ,"r"		).readlines()]
			UpperCAmelCase_	:					int     	=   [line.strip() for line in open(A__      ,"r"		).readlines()]

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

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

def 							snake_case (  A__      ,A__      ,A__		):




			def strip_title(A__		):
						if title.startswith("\""		):
									UpperCAmelCase_	:					List[str]     	=   title[1:]
						if title.endswith("\""		):
									UpperCAmelCase_	:					List[Any]     	=   title[:-1]
						return title

			UpperCAmelCase_	:					Any     	=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    A__      ,return_tensors="pt"      ,padding=A__      ,truncation=A__      ,)["input_ids"].to(args.device		)

			UpperCAmelCase_	:					List[str]     	=   rag_model.rag.question_encoder(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   question_enc_outputs[0]

			UpperCAmelCase_	:					Optional[int]     	=   rag_model.retriever(
			    A__      ,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"      ,)
			UpperCAmelCase_	:					Any     	=   rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
			UpperCAmelCase_	:					int     	=   []
			for docs in all_docs:
						UpperCAmelCase_	:					List[str]     	=   [strip_title(A__		) for title in docs["title"]]
						provenance_strings.append("\t".join(A__		)		)
			return provenance_strings

def 							snake_case (  A__      ,A__      ,A__		):




			with torch.no_grad():
						UpperCAmelCase_	:					int     	=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    A__      ,return_tensors="pt"      ,padding=A__      ,truncation=A__		)

						UpperCAmelCase_	:					Optional[Any]     	=   inputs_dict.input_ids.to(args.device		)
						UpperCAmelCase_	:					Tuple     	=   inputs_dict.attention_mask.to(args.device		)
						UpperCAmelCase_	:					List[str]     	=   rag_model.generate(  # rag_model overwrites generate
						    A__      ,attention_mask=A__      ,num_beams=args.num_beams      ,min_length=args.min_length      ,max_length=args.max_length      ,early_stopping=A__      ,num_return_sequences=1      ,bad_words_ids=[[0, 0]]      ,)
						UpperCAmelCase_	:					int     	=   rag_model.retriever.generator_tokenizer.batch_decode(A__      ,skip_special_tokens=A__		)

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

						return answers

def 							snake_case (  ):
			UpperCAmelCase_	:					int     	=   argparse.ArgumentParser()
			parser.add_argument(
			    "--model_type"      ,choices=["rag_sequence", "rag_token", "bart"]      ,type=A__      ,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=A__      ,choices=["exact", "compressed", "legacy"]      ,type=A__      ,help="RAG model retriever type"      ,)
			parser.add_argument(
			    "--index_path"      ,default=A__      ,type=A__      ,help="Path to the retrieval index"      ,)
			parser.add_argument("--n_docs"      ,default=5      ,type=A__      ,help="Number of retrieved docs"		)
			parser.add_argument(
			    "--model_name_or_path"      ,default=A__      ,type=A__      ,required=A__      ,help="Path to pretrained checkpoints or model identifier from huggingface.co/models"      ,)
			parser.add_argument(
			    "--eval_mode"      ,choices=["e2e", "retrieval"]      ,default="e2e"      ,type=A__      ,help=(
			        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
			        " precision@k."
			    )      ,)
			parser.add_argument("--k"      ,default=1      ,type=A__      ,help="k for the precision@k calculation"		)
			parser.add_argument(
			    "--evaluation_set"      ,default=A__      ,type=A__      ,required=A__      ,help="Path to a file containing evaluation samples"      ,)
			parser.add_argument(
			    "--gold_data_path"      ,default=A__      ,type=A__      ,required=A__      ,help="Path to a tab-separated file with gold samples"      ,)
			parser.add_argument(
			    "--gold_data_mode"      ,default="qa"      ,type=A__      ,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=A__      ,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=A__      ,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=A__      ,help="Number of beams to be used when generating answers"      ,)
			parser.add_argument("--min_length"      ,default=1      ,type=A__      ,help="Min length of the generated answers"		)
			parser.add_argument("--max_length"      ,default=50      ,type=A__      ,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."      ,)
			UpperCAmelCase_	:					List[Any]     	=   parser.parse_args()
			UpperCAmelCase_	:					Optional[Any]     	=   torch.device("cuda" if torch.cuda.is_available() else "cpu"		)
			return args

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   {}
			if args.model_type is None:
						UpperCAmelCase_	:					List[str]     	=   infer_model_type(args.model_name_or_path		)
						assert args.model_type is not None
			if args.model_type.startswith("rag"		):
						UpperCAmelCase_	:					Dict     	=   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
						UpperCAmelCase_	:					Any     	=   args.n_docs
						if args.index_name is not None:
									UpperCAmelCase_	:					Optional[Any]     	=   args.index_name
						if args.index_path is not None:
									UpperCAmelCase_	:					str     	=   args.index_path
			else:
						UpperCAmelCase_	:					List[str]     	=   BartForConditionalGeneration

			UpperCAmelCase_	:					Dict     	=   (
			    [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"      ,A__		)

			UpperCAmelCase_	:					Tuple     	=   get_scores if args.eval_mode == "e2e" else get_precision_at_k
			UpperCAmelCase_	:					Any     	=   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(A__      ,args.predictions_path      ,args.gold_data_path		)
									continue

						logger.info("***** Running evaluation for {} *****".format(A__		)		)
						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"		):
									UpperCAmelCase_	:					List[str]     	=   RagRetriever.from_pretrained(A__      ,**A__		)
									UpperCAmelCase_	:					int     	=   model_class.from_pretrained(A__      ,retriever=A__      ,**A__		)
									model.retriever.init_retrieval()
						else:
									UpperCAmelCase_	:					Any     	=   model_class.from_pretrained(A__      ,**A__		)
						model.to(args.device		)

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

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


if __name__ == "__main__":
			lowerCamelCase_							      = get_args()
			main(args)





"""simple docstring"""

import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       FlaubertForMultipleChoice,
       FlaubertForQuestionAnswering,
       FlaubertForQuestionAnsweringSimple,
       FlaubertForSequenceClassification,
       FlaubertForTokenClassification,
       FlaubertModel,
       FlaubertWithLMHeadModel,
   )
   from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2 ,							lowercase_=9_9 ,							lowercase_=0 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_="last" ,							lowercase_=None ,							lowercase_=None ,							)    ->       str:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_input_lengths
          UpperCAmelCase          =       use_token_type_ids
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       gelu_activation
          UpperCAmelCase          =       sinusoidal_embeddings
          UpperCAmelCase          =       causal
          UpperCAmelCase          =       asm
          UpperCAmelCase          =       n_langs
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       n_special
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       type_sequence_label_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       num_labels
          UpperCAmelCase          =       num_choices
          UpperCAmelCase          =       summary_type
          UpperCAmelCase          =       use_proj
          UpperCAmelCase          =       scope




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)
          UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

          UpperCAmelCase          =       None
          if self.use_input_lengths:
              UpperCAmelCase          =       (
                  ids_tensor([self.batch_size] ,							vocab_size=2				) + self.seq_length - 2
              )  # small variation of seq_length

          UpperCAmelCase          =       None
          if self.use_token_type_ids:
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.n_langs				)

          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          if self.use_labels:
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.type_sequence_label_size				)
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels				)
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							2				).float()
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.num_choices				)

          UpperCAmelCase          =       self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )




      def      a_   (				self				)    ->       Union[str, Any]:
          return FlaubertConfig(
              vocab_size=self.vocab_size ,							n_special=self.n_special ,							emb_dim=self.hidden_size ,							n_layers=self.num_hidden_layers ,							n_heads=self.num_attention_heads ,							dropout=self.hidden_dropout_prob ,							attention_dropout=self.attention_probs_dropout_prob ,							gelu_activation=self.gelu_activation ,							sinusoidal_embeddings=self.sinusoidal_embeddings ,							asm=self.asm ,							causal=self.causal ,							n_langs=self.n_langs ,							max_position_embeddings=self.max_position_embeddings ,							initializer_range=self.initializer_range ,							summary_type=self.summary_type ,							use_proj=self.use_proj ,							)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							lengths=lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_				)
          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Any:
          UpperCAmelCase          =       FlaubertWithLMHeadModel(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.loss.shape ,							()				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertForQuestionAnsweringSimple(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

          UpperCAmelCase          =       model(lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_				)
          self.parent.assertEqual(result.start_logits.shape ,							(self.batch_size, self.seq_length)				)
          self.parent.assertEqual(result.end_logits.shape ,							(self.batch_size, self.seq_length)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       int:
          UpperCAmelCase          =       FlaubertForQuestionAnswering(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

          UpperCAmelCase          =       model(
              lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_ ,							cls_index=lowercase_ ,							is_impossible=lowercase_ ,							p_mask=lowercase_ ,							)

          UpperCAmelCase          =       model(
              lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_ ,							cls_index=lowercase_ ,							is_impossible=lowercase_ ,							)

          ((UpperCAmelCase)      ,				)          =       result_with_labels.to_tuple()

          UpperCAmelCase          =       model(lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_				)

          ((UpperCAmelCase)      ,				)          =       result_with_labels.to_tuple()

          self.parent.assertEqual(result_with_labels.loss.shape ,							()				)
          self.parent.assertEqual(result.start_top_log_probs.shape ,							(self.batch_size, model.config.start_n_top)				)
          self.parent.assertEqual(result.start_top_index.shape ,							(self.batch_size, model.config.start_n_top)				)
          self.parent.assertEqual(
              result.end_top_log_probs.shape ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
          self.parent.assertEqual(
              result.end_top_index.shape ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
          self.parent.assertEqual(result.cls_logits.shape ,							(self.batch_size,)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Dict:
          UpperCAmelCase          =       FlaubertForSequenceClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							labels=lowercase_				)

          self.parent.assertEqual(result.loss.shape ,							()				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       FlaubertForTokenClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Tuple:
          UpperCAmelCase          =       self.num_choices
          UpperCAmelCase          =       FlaubertForMultipleChoice(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       input_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       token_type_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       input_mask.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_ ,							)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				
          )          =       config_and_inputs
          UpperCAmelCase          =       {
              'input_ids': input_ids,
              'token_type_ids': token_type_ids,
              'lengths': input_lengths,
              'attention_mask': input_mask,
          }
          return config, inputs_dict

@require_torch
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						(
          (
              FlaubertModel,
              FlaubertWithLMHeadModel,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				str				   =						(
          {
              "feature-extraction": FlaubertModel,
              "fill-mask": FlaubertWithLMHeadModel,
              "question-answering": FlaubertForQuestionAnsweringSimple,
              "text-classification": FlaubertForSequenceClassification,
              "token-classification": FlaubertForTokenClassification,
              "zero-shot": FlaubertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith('Fast'				)
          ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True

          return False




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=False				)    ->       List[str]:
          UpperCAmelCase          =       super()._prepare_for_class(lowercase_ ,							lowercase_ ,							return_labels=lowercase_				)

          if return_labels:
              if model_class.__name__ == "FlaubertForQuestionAnswering":
                  UpperCAmelCase          =       torch.zeros(
                      self.model_tester.batch_size ,							dtype=torch.long ,							device=lowercase_				)
                  UpperCAmelCase          =       torch.zeros(
                      self.model_tester.batch_size ,							dtype=torch.long ,							device=lowercase_				)

          return inputs_dict




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							emb_dim=3_7				)




      def      a_   (				self				)    ->       Optional[Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_model(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_lm_head(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_simple_qa(*lowercase_				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_qa(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_token_classif(*lowercase_				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase_				)




      @slow
      def      a_   (				self				)    ->       List[str]:
          for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase          =       FlaubertModel.from_pretrained(lowercase_				)
              self.assertIsNotNone(lowercase_				)




      @slow
      @require_torch_gpu
      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == FlaubertForMultipleChoice:
                  return

              UpperCAmelCase          =       True
              UpperCAmelCase          =       model_class(config=lowercase_				)

              UpperCAmelCase          =       self._prepare_for_class(lowercase_ ,							lowercase_				)
              UpperCAmelCase          =       torch.jit.trace(
                  lowercase_ ,							(inputs_dict['input_ids'].to('cpu'				), inputs_dict['attention_mask'].to('cpu'				))				)

              with tempfile.TemporaryDirectory() as tmp:
                  torch.jit.save(lowercase_ ,							os.path.join(lowercase_ ,							'traced_model.pt'				)				)
                  UpperCAmelCase          =       torch.jit.load(os.path.join(lowercase_ ,							'traced_model.pt'				) ,							map_location=lowercase_				)
                  loaded(inputs_dict['input_ids'].to(lowercase_				) ,							inputs_dict['attention_mask'].to(lowercase_				)				)

@require_torch
class   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'				)
          UpperCAmelCase          =       torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]				)
          with torch.no_grad():
              UpperCAmelCase          =       model(lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 1_1, 7_6_8)				)
          self.assertEqual(output.shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor(
              [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]				)

          self.assertTrue(torch.allclose(output[:, :3, :3] ,							lowercase_ ,							atol=1E-4				)				)



