import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     _SCREAMING_SNAKE_CASE     =     """platform"""

     import jax
     import jax.numpy as jnp

     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None			,					lowerCamelCase__=None       )					->Optional[int]:

  '''simple docstring'''




  if attention_mask is None:
    a__  		=  np.where(input_ids != config.pad_token_id			,					1			,					0       )
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }







@require_flax
class        UpperCAmelCase_					:




    def __init__(       self	: Optional[Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Union[str, Any]=13   ,   lowerCAmelCase	: Optional[Any]=7   ,   lowerCAmelCase	: List[str]=True   ,   lowerCAmelCase	: Optional[int]=False   ,   lowerCAmelCase	: List[Any]=99   ,   lowerCAmelCase	: Optional[int]=16   ,   lowerCAmelCase	: Union[str, Any]=2   ,   lowerCAmelCase	: Optional[int]=4   ,   lowerCAmelCase	: Any=4   ,   lowerCAmelCase	: Union[str, Any]="gelu"   ,   lowerCAmelCase	: Tuple=0.1   ,   lowerCAmelCase	: Optional[int]=0.1   ,   lowerCAmelCase	: str=20   ,   lowerCAmelCase	: Optional[int]=2   ,   lowerCAmelCase	: Union[str, Any]=1   ,   lowerCAmelCase	: List[Any]=0   ,   lowerCAmelCase	: int=16   ,   lowerCAmelCase	: int=16   ,   lowerCAmelCase	: Tuple=0.02   ,   )						->			Union[str, Any]:
      a__  		=  parent
      a__  		=  batch_size
      a__  		=  seq_length
      a__  		=  is_training
      a__  		=  use_labels
      a__  		=  vocab_size
      a__  		=  hidden_size
      a__  		=  num_hidden_layers
      a__  		=  num_attention_heads
      a__  		=  intermediate_size
      a__  		=  hidden_act
      a__  		=  hidden_dropout_prob
      a__  		=  attention_probs_dropout_prob
      a__  		=  max_position_embeddings
      a__  		=  eos_token_id
      a__  		=  pad_token_id
      a__  		=  bos_token_id
      a__  		=  embed_dim
      a__  		=  word_embed_proj_dim
      a__  		=  initializer_range
      a__  		=  False




    def     __lowerCamelCase			(       self	: Tuple )						->			str:
      a__  		=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size )   ,   3   ,   self.vocab_size )
      a__  		=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,   dtype=np.intaa ))   ,   -1 )

      a__  		=  OPTConfig(
          vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   ffn_dim=self.intermediate_size   ,   dropout=self.hidden_dropout_prob   ,   attention_dropout=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   eos_token_id=self.eos_token_id   ,   bos_token_id=self.bos_token_id   ,   pad_token_id=self.pad_token_id   ,   embed_dim=self.embed_dim   ,   is_encoder_decoder=lowerCAmelCase   ,   word_embed_proj_dim=self.word_embed_proj_dim   ,   initializer_range=self.initializer_range   ,   use_cache=lowerCAmelCase   ,   )
      a__  		=  prepare_opt_inputs_dict(lowerCAmelCase   ,   lowerCAmelCase )
      return config, inputs_dict




    def     __lowerCamelCase			(       self	: Tuple )						->			int:
      a__		,a__  		=  self.prepare_config_and_inputs()
      return config, inputs_dict




    def     __lowerCamelCase			(       self	: Optional[Any]   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Optional[int] )						->			Tuple:
      a__  		=  20
      a__  		=  model_class_name(lowerCAmelCase )

      a__  		=  inputs_dict["input_ids"]
      a__  		=  inputs_dict["attention_mask"]

      a__  		=  model.init_cache(input_ids.shape[0]   ,   lowerCAmelCase )
      a__  		=  jnp.ones((input_ids.shape[0], max_length)   ,   dtype="i4" )

      a__  		=  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,   (input_ids.shape[0], input_ids.shape[-1] - 1)   ,   )
      a__  		=  model(
          input_ids[:, :-1]   ,   attention_mask=lowerCAmelCase   ,   past_key_values=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   )

      a__  		=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,   dtype="i4" )
      a__  		=  model(
          input_ids[:, -1:]   ,   attention_mask=lowerCAmelCase   ,   past_key_values=outputs_cache.past_key_values   ,   position_ids=lowerCAmelCase   ,   )

      a__  		=  model(lowerCAmelCase )

      a__  		=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3   ,   msg=f"Max diff is {diff}" )




    def     __lowerCamelCase			(       self	: List[Any]   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Optional[int]:
      a__  		=  20
      a__  		=  model_class_name(lowerCAmelCase )

      a__		,a__  		=  (
          inputs_dict["input_ids"],
          inputs_dict["attention_mask"],
      )

      a__  		=  jnp.concatenate(
          [
              attention_mask,
              jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
          ]   ,   axis=-1   ,   )

      a__  		=  model.init_cache(input_ids.shape[0]   ,   lowerCAmelCase )
      a__  		=  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,   (input_ids.shape[0], input_ids.shape[-1] - 1)   ,   )

      a__  		=  model(
          input_ids[:, :-1]   ,   attention_mask=lowerCAmelCase   ,   past_key_values=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   )
      a__  		=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,   dtype="i4" )
      a__  		=  model(
          input_ids[:, -1:]   ,   past_key_values=outputs_cache.past_key_values   ,   attention_mask=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   )

      a__  		=  model(lowerCAmelCase   ,   attention_mask=lowerCAmelCase )

      a__  		=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3   ,   msg=f"Max diff is {diff}" )







@require_flax
class        UpperCAmelCase_					(				_a  ,  unittest.TestCase  ,  _a     ):
    UpperCAmelCase_      :							str    			=				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    UpperCAmelCase_      :							Dict    			=				() if is_flax_available() else ()




    def     __lowerCamelCase			(       self	: Any )						->			Any:
      a__  		=  FlaxOPTModelTester(self )




    def     __lowerCamelCase			(       self	: List[str] )						->			List[str]:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Optional[Any] )						->			Optional[Any]:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )




    @slow
    def     __lowerCamelCase			(       self	: Optional[Any] )						->			List[Any]:
      for model_class_name in self.all_model_classes:
        a__  		=  model_class_name.from_pretrained("facebook/opt-125m" )
        a__  		=  np.ones((1, 1) ) * model.config.eos_token_id
        a__  		=  model(lowerCAmelCase )
        self.assertIsNotNone(lowerCAmelCase )







@require_sentencepiece
@require_flax
class        UpperCAmelCase_					(				unittest.TestCase     ):




    @slow
    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			Optional[Any]:
      a__  		=  FlaxOPTModel.from_pretrained("facebook/opt-350m" )
      a__  		=  jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
      a__  		=  model(input_ids=lowerCAmelCase ).last_hidden_state
      a__  		=  (1, 11, 512)
      self.assertEqual(output.shape   ,   lowerCAmelCase )
      a__  		=  jnp.array(
          [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]] )
      self.assertTrue(jnp.allclose(output[:, :3, :3]   ,   lowerCAmelCase   ,   atol=4e-2 ) )







@require_flax
@slow
class        UpperCAmelCase_					(				unittest.TestCase     ):




    def     __lowerCamelCase			(       self	: Optional[Any] )						->			str:
      super().setUp()
      a__  		=  "facebook/opt-350m"




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			Dict:
      a__  		=  FlaxOPTForCausalLM.from_pretrained(self.path_model )
      a__  		=  GPTaTokenizer.from_pretrained(self.path_model )

      a__  		=  [
          "Today is a beautiful day and I want to",
          "In the city of",
          "Paris is the capital of France and",
          "Computers and mobile phones have taken",
      ]
      # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
      a__  		=  tokenizer(lowerCAmelCase   ,   return_tensors="jax"   ,   padding=lowerCAmelCase   ,   add_special_tokens=lowerCAmelCase )
      a__  		=  model(inputs.input_ids   ,   attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
      a__  		=  jnp.array(
          [
              [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
              [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
              [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
              [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
          ] )
      self.assertTrue(jnp.allclose(lowerCAmelCase   ,   lowerCAmelCase   ,   atol=4e-2 ) )

      a__  		=  jax.jit(lowerCAmelCase )
      a__  		=  model(inputs.input_ids   ,   attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
      self.assertTrue(jnp.allclose(lowerCAmelCase   ,   lowerCAmelCase   ,   atol=4e-2 ) )







@require_flax
@slow
class        UpperCAmelCase_					(				unittest.TestCase     ):




    @property
    def     __lowerCamelCase			(       self	: str )						->			Any:
      return [
          "Today is a beautiful day and I want",
          "In the city of",
          "Paris is the capital of France and",
          "Computers and mobile phones have taken",
      ]




    def     __lowerCamelCase			(       self	: List[str] )						->			Any:
      a__  		=  "facebook/opt-125m"

      a__  		=  [
          "Today is a beautiful day and I want to",
          "In the city of New York, the city",
          "Paris is the capital of France and the capital",
          "Computers and mobile phones have taken over the",
      ]

      a__  		=  []

      a__  		=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase )
      a__  		=  GPTaTokenizer.from_pretrained(lowerCAmelCase )

      for prompt in self.prompts:
        a__  		=  tokenizer(lowerCAmelCase   ,   return_tensors="jax" ).input_ids

        a__  		=  model.generate(lowerCAmelCase   ,   max_length=10 )
        a__  		=  generated_ids[0]

        a__  		=  tokenizer.batch_decode(lowerCAmelCase   ,   skip_special_tokens=lowerCAmelCase )
        predicted_outputs += generated_string

      self.assertListEqual(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[Any] )						->			Optional[int]:
      a__  		=  "facebook/opt-350m"

      a__  		=  [
          "Today is a beautiful day and I want to",
          "In the city of San Francisco, the city",
          "Paris is the capital of France and the capital",
          "Computers and mobile phones have taken over the",
      ]

      a__  		=  []
      a__  		=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase )
      a__  		=  GPTaTokenizer.from_pretrained(lowerCAmelCase )

      for prompt in self.prompts:
        a__  		=  tokenizer(lowerCAmelCase   ,   return_tensors="jax" ).input_ids

        a__  		=  model.generate(lowerCAmelCase   ,   max_length=10 )
        a__  		=  generated_ids[0]

        a__  		=  tokenizer.batch_decode(lowerCAmelCase   ,   skip_special_tokens=lowerCAmelCase )
        predicted_outputs += generated_string

      self.assertListEqual(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Optional[int] )						->			str:
      a__  		=  "facebook/opt-125m"
      a__  		=  [
          "Today is a beautiful day and I want to thank",
          "In the city of Rome Canaver Canaver Canaver Canaver",
      ]
      a__  		=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase )
      a__  		=  GPTaTokenizer.from_pretrained(lowerCAmelCase )
      a__  		=  tokenizer(
          [
              "Today is a beautiful day and I want to",
              "In the city of",
          ]   ,   return_tensors="jax"   ,   padding=lowerCAmelCase   ,   )

      a__  		=  jax.jit(model.generate )

      a__  		=  jit_generate(inputs["input_ids"]   ,   attention_mask=inputs["attention_mask"] ).sequences

      a__  		=  tokenizer.batch_decode(lowerCAmelCase   ,   skip_special_tokens=lowerCAmelCase )

      self.assertIsNotNone(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: int )						->			List[str]:
      a__  		=  "facebook/opt-350m"

      a__  		=  GPTaTokenizer.from_pretrained(lowerCAmelCase )
      a__  		=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase )

      a__  		=  "left"

      # use different length sentences to test batching
      a__  		=  [
          "Hello, my dog is a little",
          "Today, I",
      ]

      a__  		=  tokenizer(lowerCAmelCase   ,   return_tensors="jax"   ,   padding=lowerCAmelCase )
      a__  		=  inputs["input_ids"]

      a__  		=  model.generate(input_ids=lowerCAmelCase   ,   attention_mask=inputs["attention_mask"]   ,   trace=lowerCAmelCase )

      a__  		=  tokenizer(sentences[0]   ,   return_tensors="jax" ).input_ids
      a__  		=  model.generate(input_ids=lowerCAmelCase )

      a__  		=  inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
      a__  		=  tokenizer(sentences[1]   ,   return_tensors="jax" ).input_ids
      a__  		=  model.generate(input_ids=lowerCAmelCase   ,   max_length=model.config.max_length - num_paddings )

      a__  		=  tokenizer.batch_decode(outputs[0]   ,   skip_special_tokens=lowerCAmelCase )
      a__  		=  tokenizer.decode(output_non_padded[0][0]   ,   skip_special_tokens=lowerCAmelCase )
      a__  		=  tokenizer.decode(output_padded[0][0]   ,   skip_special_tokens=lowerCAmelCase )

      a__  		=  [
          "Hello, my dog is a little bit of a dork.\nI'm a little bit",
          "Today, I was in the middle of a conversation with a friend about the",
      ]
      self.assertListEqual(lowerCAmelCase   ,   lowerCAmelCase )
      self.assertListEqual(lowerCAmelCase   ,   [non_padded_sentence, padded_sentence] )


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


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

transformers_logging.set_verbosity_info()
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  return max(metric_fn(lowerCamelCase__			,					lowerCamelCase__       ) for gt in ground_truths       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
  a__  		=  []

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

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

  a__  		=  100.0 * em / total
  a__  		=  100.0 * fa / total

  logger.info(f"F1: {fa:.2f}"       )
  logger.info(f"EM: {em:.2f}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Tuple:

  '''simple docstring'''




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

  a__  		=  a__  		=  0
  for hypo, reference in zip(lowerCamelCase__			,					lowerCamelCase__       ):
    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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->str:

  '''simple docstring'''




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

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

  a__  		=  rag_model.rag.question_encoder(lowerCamelCase__       )
  a__  		=  question_enc_outputs[0]

  a__  		=  rag_model.retriever(
      lowerCamelCase__			,					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(lowerCamelCase__       ) for title in docs["title"]]
    provenance_strings.append("\t".join(lowerCamelCase__       )       )
  return provenance_strings
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




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

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

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

    return answers
def 					__lowercase						(    )					->str:

  '''simple docstring'''




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

  '''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"			,					lowerCamelCase__       )

  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(lowerCamelCase__			,					args.predictions_path			,					args.gold_data_path       )
      continue

    logger.info("***** Running evaluation for {} *****".format(lowerCamelCase__       )       )
    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(lowerCamelCase__			,					**lowerCamelCase__       )
      a__  		=  model_class.from_pretrained(lowerCamelCase__			,					retriever=lowerCamelCase__			,					**lowerCamelCase__       )
      model.retriever.init_retrieval()
    else:
      a__  		=  model_class.from_pretrained(lowerCamelCase__			,					**lowerCamelCase__       )
    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(lowerCamelCase__       ):
        questions.append(line.strip()       )
        if len(lowerCamelCase__       ) == args.eval_batch_size:
          a__  		=  evaluate_batch_fn(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
          preds_file.write("\n".join(lowerCamelCase__       ) + "\n"       )
          preds_file.flush()
          a__  		=  []
      if len(lowerCamelCase__       ) > 0:
        a__  		=  evaluate_batch_fn(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
        preds_file.write("\n".join(lowerCamelCase__       )       )
        preds_file.flush()

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


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

