



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 	_lowercase							:





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						return None




class 	_lowercase							:





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						return None




class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""
	__A							=							[
	    # (model_name, model_kwargs)
	    ("bert-base-cased", {}),
	    ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
	]




	@require_tf
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowerCamelCase_		,       "tf"		,       12		,       **lowerCamelCase_		)




	@require_torch
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowerCamelCase_		,       "pt"		,       12		,       **lowerCamelCase_		)




	@require_torch
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import BertModel

						a					     =   ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
						with NamedTemporaryFile(mode="w+t"		) as vocab_file:
											vocab_file.write("\n".join(lowerCamelCase_		)		)
											vocab_file.flush()
											a					     =   BertTokenizerFast(vocab_file.name		)

						with TemporaryDirectory() as bert_save_dir:
											a					     =   BertModel(BertConfig(vocab_size=len(lowerCamelCase_		)		)		)
											model.save_pretrained(lowerCamelCase_		)
											self._test_export(lowerCamelCase_		,       "pt"		,       12		,       lowerCamelCase_		)




	@require_tf
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											a					     =   self._test_export(lowerCamelCase_		,       "tf"		,       12		,       **lowerCamelCase_		)
											a					     =   quantize(Path(lowerCamelCase_		)		)

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(lowerCamelCase_		).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"		)




	@require_torch
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											a					     =   self._test_export(lowerCamelCase_		,       "pt"		,       12		,       **lowerCamelCase_		)
											a					     =   quantize(lowerCamelCase_		)

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(lowerCamelCase_		).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						try:
											# Compute path
											with TemporaryDirectory() as tempdir:
																a					     =   Path(lowerCamelCase_		).joinpath("model.onnx"		)

											# Remove folder if exists
											if path.parent.exists():
																path.parent.rmdir()

											# Export
											convert(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		)

											return path
						except Exception as e:
											self.fail(lowerCamelCase_		)




	@require_torch
	@require_tokenizers
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import BertModel

						a					     =   BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"		)		)
						a					     =   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"		)
						self._test_infer_dynamic_axis(lowerCamelCase_		,       lowerCamelCase_		,       "pt"		)




	@require_tf
	@require_tokenizers
	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import TFBertModel

						a					     =   TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"		)		)
						a					     =   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"		)
						self._test_infer_dynamic_axis(lowerCamelCase_		,       lowerCamelCase_		,       "tf"		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FeatureExtractionPipeline(lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
						a     ,				a     ,				a     ,				a					     =   infer_shapes(lowerCamelCase_		,       lowerCamelCase_		)

						# Assert all variables are present
						self.assertEqual(len(lowerCamelCase_		)		,       len(lowerCamelCase_		)		)
						self.assertTrue(all(var_name in shapes for var_name in variable_names		)		)
						self.assertSequenceEqual(variable_names[:3]		,       lowerCamelCase_		)
						self.assertSequenceEqual(variable_names[3:]		,       lowerCamelCase_		)

						# Assert inputs are {0: batch, 1: sequence}
						for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]		,       {0: "batch", 1: "sequence"}		)

						# Assert outputs are {0: batch, 1: sequence} and {0: batch}
						self.assertDictEqual(shapes["output_0"]		,       {0: "batch", 1: "sequence"}		)
						self.assertDictEqual(shapes["output_1"]		,       {0: "batch"}		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ["input_ids", "attention_mask", "token_type_ids"]
						a					     =   {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
						a     ,				a					     =   ensure_valid_input(FuncContiguousArgs()		,       lowerCamelCase_		,       lowerCamelCase_		)

						# Should have exactly the same number of args (all are valid)
						self.assertEqual(len(lowerCamelCase_		)		,       3		)

						# Should have exactly the same input names
						self.assertEqual(set(lowerCamelCase_		)		,       set(lowerCamelCase_		)		)

						# Parameter should be reordered according to their respective place in the function:
						# (input_ids, token_type_ids, attention_mask)
						self.assertEqual(lowerCamelCase_		,       (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])		)

						# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
						a     ,				a					     =   ensure_valid_input(FuncNonContiguousArgs()		,       lowerCamelCase_		,       lowerCamelCase_		)

						# Should have exactly the one arg (all before the one not provided "some_other_args")
						self.assertEqual(len(lowerCamelCase_		)		,       1		)
						self.assertEqual(len(lowerCamelCase_		)		,       1		)

						# Should have only "input_ids"
						self.assertEqual(inputs_args[0]		,       tokens["input_ids"]		)
						self.assertEqual(ordered_input_names[0]		,       "input_ids"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   generate_identified_filename(Path("/home/something/my_fake_model.onnx"		)		,       "-test"		)
						self.assertEqual("/home/something/my_fake_model-test.onnx"		,       generated.as_posix()		)





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


_lowercase: Tuple      			=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def     a(      A       :    Optional[Any]       )		->       str:
					"""simple docstring"""





					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(      A       :    Dict ,     A       :    List[Any] ,     A       :    str       )		->       List[str]:
					"""simple docstring"""





					return max(metric_fn(A ,     A       ) for gt in ground_truths       )






def     a(      A       :    str ,     A       :    Optional[Any] ,     A       :    Optional[Any]       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]
					a					     =   []

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

					a					     =   a					     =   a					     =   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       )

					a					     =   100.0 * em / total
					a					     =   100.0 * fa / total

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






def     a(      A       :    Dict ,     A       :    str ,     A       :    List[str]       )		->       List[Any]:
					"""simple docstring"""





					a					     =   args.k
					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]
					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]

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

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






def     a(      A       :    Dict ,     A       :    Any ,     A       :    List[Any]       )		->       Any:
					"""simple docstring"""





					def strip_title(A       :    Any       ):
										if title.startswith("\""       ):
															a					     =   title[1:]
										if title.endswith("\""       ):
															a					     =   title[:-1]
										return title

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

					a					     =   rag_model.rag.question_encoder(A       )
					a					     =   question_enc_outputs[0]

					a					     =   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" ,     )
					a					     =   rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
					a					     =   []
					for docs in all_docs:
										a					     =   [strip_title(A       ) for title in docs["title"]]
										provenance_strings.append("\t".join(A       )       )
					return provenance_strings






def     a(      A       :    Union[str, Any] ,     A       :    Optional[int] ,     A       :    Tuple       )		->       Tuple:
					"""simple docstring"""





					with torch.no_grad():
										a					     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    A ,     return_tensors="pt" ,     padding=A ,     truncation=A       )

										a					     =   inputs_dict.input_ids.to(args.device       )
										a					     =   inputs_dict.attention_mask.to(args.device       )
										a					     =   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]] ,     )
										a					     =   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     a(      )		->       Any:
					"""simple docstring"""





					a					     =   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." ,     )
					a					     =   parser.parse_args()
					a					     =   torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
					return args






def     a(      A       :    Any       )		->       Optional[Any]:
					"""simple docstring"""





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

					a					     =   (
					    [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       )

					a					     =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
					a					     =   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"       ):
															a					     =   RagRetriever.from_pretrained(A ,     **A       )
															a					     =   model_class.from_pretrained(A ,     retriever=A ,     **A       )
															model.retriever.init_retrieval()
										else:
															a					     =   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:
															a					     =   []
															for line in tqdm(A       ):
																				questions.append(line.strip()       )
																				if len(A       ) == args.eval_batch_size:
																									a					     =   evaluate_batch_fn(A ,     A ,     A       )
																									preds_file.write("\n".join(A       ) + "\n"       )
																									preds_file.flush()
																									a					     =   []
															if len(A       ) > 0:
																				a					     =   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__":
				_lowercase: Optional[int]      			=		get_args()
				main(args)
