'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						List[str]=True ,		__lowerCamelCase			:						List[Any]="pt"				)	->							Optional[Any]:
      '''simple docstring'''


      lowercase_     =      {"add_prefix_space": True} if isinstance(__lowerCamelCase ,		__lowerCamelCase				) and not line.startswith(" "				) else {}
      lowercase_     =      padding_side
      return tokenizer(
          [line] ,		max_length=__lowerCamelCase ,		padding="max_length" if pad_to_max_length else None ,		truncation=__lowerCamelCase ,		return_tensors=__lowerCamelCase ,		add_special_tokens=__lowerCamelCase ,		**__lowerCamelCase ,		)


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str=None ,		)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      input_ids.ne(__lowerCamelCase				).any(dim=0				)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any]="train"   ,		__lowerCAmelCase				:	Dict=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	List[str]=None   ,		__lowerCAmelCase				:	List[Any]=""   ,		)      ->					Union[str, Any]:
         super().__init__()
         lowercase_     =      Path(__lowerCAmelCase).joinpath(type_path + ".source")
         lowercase_     =      Path(__lowerCAmelCase).joinpath(type_path + ".target")
         lowercase_     =      self.get_char_lens(self.src_file)
         lowercase_     =      max_source_length
         lowercase_     =      max_target_length
         assert min(self.src_lens) > 0, F'found empty line in {self.src_file}'
         lowercase_     =      tokenizer
         lowercase_     =      prefix
         if n_obs is not None:
               lowercase_     =      self.src_lens[:n_obs]
         lowercase_     =      src_lang
         lowercase_     =      tgt_lang


   def __len__(   self				:	Any)      ->					Any:
         return len(self.src_lens)


   def __getitem__(   self				:	Any   ,		__lowerCAmelCase				:	Optional[int])      ->					Dict[str, torch.Tensor]:
         lowercase_     =      index + 1  # linecache starts at 1
         lowercase_     =      self.prefix + linecache.getline(str(self.src_file)   ,		__lowerCAmelCase).rstrip("\n")
         lowercase_     =      linecache.getline(str(self.tgt_file)   ,		__lowerCAmelCase).rstrip("\n")
         assert source_line, F'empty source line for index {index}'
         assert tgt_line, F'empty tgt line for index {index}'

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer   ,		__lowerCAmelCase):
               source_line += self.tokenizer.eos_token
               tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         lowercase_     =      (
             self.tokenizer.question_encoder if isinstance(self.tokenizer   ,		__lowerCAmelCase) else self.tokenizer
         )
         lowercase_     =      self.tokenizer.generator if isinstance(self.tokenizer   ,		__lowerCAmelCase) else self.tokenizer

         lowercase_     =      encode_line(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.max_source_length   ,		"right")
         lowercase_     =      encode_line(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.max_target_length   ,		"right")

         lowercase_     =      source_inputs["input_ids"].squeeze()
         lowercase_     =      target_inputs["input_ids"].squeeze()
         lowercase_     =      source_inputs["attention_mask"].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }


   @staticmethod
   def __UpperCAmelCase       (   __lowerCAmelCase				:	int)      ->					Dict:
         return [len(__lowerCAmelCase) for x in Path(__lowerCAmelCase).open().readlines()]


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[str])      ->					Dict[str, torch.Tensor]:
         lowercase_     =      torch.stack([x["input_ids"] for x in batch])
         lowercase_     =      torch.stack([x["attention_mask"] for x in batch])
         lowercase_     =      torch.stack([x["decoder_input_ids"] for x in batch])
         lowercase_     =      (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer   ,		__lowerCAmelCase)
             else self.tokenizer.pad_token_id
         )
         lowercase_     =      (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer   ,		__lowerCAmelCase)
             else self.tokenizer.pad_token_id
         )
         lowercase_     =      trim_batch(__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_ ,							lowercase_     =      trim_batch(__lowerCAmelCase   ,		__lowerCAmelCase   ,		attention_mask=__lowerCAmelCase)
         lowercase_     =      {
             "input_ids": source_ids,
             "attention_mask": source_mask,
             "decoder_input_ids": y,
         }
         return batch




lowerCAmelCase_ : Union[str, Any]						  =      getLogger(__name__)


def __a (	__lowerCamelCase			:						List[List]				)	->							Optional[int]:
      '''simple docstring'''


      return list(itertools.chain.from_iterable(__lowerCamelCase				)				)


def __a (	__lowerCamelCase			:						str				)	->							None:
      '''simple docstring'''


      lowercase_     =      get_git_info()
      save_json(__lowerCamelCase ,		os.path.join(__lowerCamelCase ,		"git_log.json"				)				)


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Union[str, Any]=4 ,		**__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


      with open(__lowerCamelCase ,		"w"				) as f:
            json.dump(__lowerCamelCase ,		__lowerCamelCase ,		indent=__lowerCamelCase ,		**__lowerCamelCase				)


def __a (	__lowerCamelCase			:						int				)	->							Tuple:
      '''simple docstring'''


      with open(__lowerCamelCase				) as f:
            return json.load(__lowerCamelCase				)


def __a (	)	->							Any:
      '''simple docstring'''


      lowercase_     =      git.Repo(search_parent_directories=__lowerCamelCase				)
      lowercase_     =      {
          "repo_id": str(__lowerCamelCase				),
          "repo_sha": str(repo.head.object.hexsha				),
          "repo_branch": str(repo.active_branch				),
          "hostname": str(socket.gethostname()				),
      }
      return repo_infos


def __a (	__lowerCamelCase			:						Callable ,		__lowerCamelCase			:						Iterable				)	->							List:
      '''simple docstring'''


      return list(map(__lowerCamelCase ,		__lowerCamelCase				)				)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Union[str, Any]:
      '''simple docstring'''


      with open(__lowerCamelCase ,		"wb"				) as f:
            return pickle.dump(__lowerCamelCase ,		__lowerCamelCase				)


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Optional[Any]:
      '''simple docstring'''


      def remove_articles(__lowerCamelCase			:						Tuple				):
            return re.sub(R"\b(a|an|the)\b" ,		" " ,		__lowerCamelCase				)

      def white_space_fix(__lowerCamelCase			:						Optional[Any]				):
            return " ".join(text.split()				)

      def remove_punc(__lowerCamelCase			:						List[str]				):
            lowercase_     =      set(string.punctuation				)
            return "".join(ch for ch in text if ch not in exclude				)

      def lower(__lowerCamelCase			:						Dict				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase				)				)				)				)


def __a (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Tuple				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      normalize_answer(__lowerCamelCase				).split()
      lowercase_     =      normalize_answer(__lowerCamelCase				).split()
      lowercase_     =      Counter(__lowerCamelCase				) & Counter(__lowerCamelCase				)
      lowercase_     =      sum(common.values()				)
      if num_same == 0:
            return 0
      lowercase_     =      1.0 * num_same / len(__lowerCamelCase				)
      lowercase_     =      1.0 * num_same / len(__lowerCamelCase				)
      lowercase_     =      (2 * precision * recall) / (precision + recall)
      return fa


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						List[Any]				)	->							Dict:
      '''simple docstring'''


      return normalize_answer(__lowerCamelCase				) == normalize_answer(__lowerCamelCase				)


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[str]				)	->							Dict:
      '''simple docstring'''


      assert len(__lowerCamelCase				) == len(__lowerCamelCase				)
      lowercase_     =      0
      for hypo, pred in zip(__lowerCamelCase ,		__lowerCamelCase				):
            em += exact_match_score(__lowerCamelCase ,		__lowerCamelCase				)
      if len(__lowerCamelCase				) > 0:
            em /= len(__lowerCamelCase				)
      return {"em": em}


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Tuple:
      '''simple docstring'''


      return model_prefix.startswith("rag"				)






def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      {p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      lowercase_     =      "dropout_rate"
      for p in extra_params:
            if getattr(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				):
                  if not hasattr(__lowerCamelCase ,		__lowerCamelCase				) and not hasattr(__lowerCamelCase ,		equivalent_param[p]				):
                        logger.info("config doesn't have a `{}` attribute".format(__lowerCamelCase				)				)
                        delattr(__lowerCamelCase ,		__lowerCamelCase				)
                        continue
                  lowercase_     =      p if hasattr(__lowerCamelCase ,		__lowerCamelCase				) else equivalent_param[p]
                  setattr(__lowerCamelCase ,		__lowerCamelCase ,		getattr(__lowerCamelCase ,		__lowerCamelCase				)				)
                  delattr(__lowerCamelCase ,		__lowerCamelCase				)
      return hparams, config







'''simple docstring'''

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
           CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           CTRLForSequenceClassification,
           CTRLLMHeadModel,
           CTRLModel,
       )





class 							lowercase						:


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any=14   ,		__lowerCAmelCase				:	Optional[Any]=7   ,		__lowerCAmelCase				:	List[str]=True   ,		__lowerCAmelCase				:	Optional[int]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Any=99   ,		__lowerCAmelCase				:	Tuple=32   ,		__lowerCAmelCase				:	Union[str, Any]=5   ,		__lowerCAmelCase				:	str=4   ,		__lowerCAmelCase				:	Dict=37   ,		__lowerCAmelCase				:	Optional[int]="gelu"   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	Tuple=0.1   ,		__lowerCAmelCase				:	str=512   ,		__lowerCAmelCase				:	Optional[Any]=16   ,		__lowerCAmelCase				:	Optional[int]=2   ,		__lowerCAmelCase				:	str=0.02   ,		__lowerCAmelCase				:	str=3   ,		__lowerCAmelCase				:	Tuple=4   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		)      ->					List[Any]:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      seq_length
         lowercase_     =      is_training
         lowercase_     =      use_token_type_ids
         lowercase_     =      use_input_mask
         lowercase_     =      use_labels
         lowercase_     =      use_mc_token_ids
         lowercase_     =      vocab_size
         lowercase_     =      hidden_size
         lowercase_     =      num_hidden_layers
         lowercase_     =      num_attention_heads
         lowercase_     =      intermediate_size
         lowercase_     =      hidden_act
         lowercase_     =      hidden_dropout_prob
         lowercase_     =      attention_probs_dropout_prob
         lowercase_     =      max_position_embeddings
         lowercase_     =      type_vocab_size
         lowercase_     =      type_sequence_label_size
         lowercase_     =      initializer_range
         lowercase_     =      num_labels
         lowercase_     =      num_choices
         lowercase_     =      scope
         lowercase_     =      self.vocab_size - 1


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size)

         lowercase_     =      None
         if self.use_input_mask:
               lowercase_     =      random_attention_mask([self.batch_size, self.seq_length])

         lowercase_     =      None
         if self.use_token_type_ids:
               lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.type_vocab_size)

         lowercase_     =      None
         if self.use_mc_token_ids:
               lowercase_     =      ids_tensor([self.batch_size, self.num_choices]   ,		self.seq_length)

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

         lowercase_     =      self.get_config()

         lowercase_     =      ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,		2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         return CTRLConfig(
             vocab_size=self.vocab_size   ,		n_embd=self.hidden_size   ,		n_layer=self.num_hidden_layers   ,		n_head=self.num_attention_heads   ,		n_positions=self.max_position_embeddings   ,		pad_token_id=self.pad_token_id   ,		)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Union[str, Any]   ,		*__lowerCAmelCase				:	Tuple)      ->					List[str]:
         lowercase_     =      CTRLModel(config=__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		head_mask=__lowerCAmelCase)
         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase)
         lowercase_     =      model(__lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(len(result.past_key_values)   ,		config.n_layer)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Dict   ,		*__lowerCAmelCase				:	int)      ->					int:
         lowercase_     =      CTRLLMHeadModel(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					int:
         lowercase_     =      self.prepare_config_and_inputs()

         (
             (
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							
         )     =      config_and_inputs

         lowercase_     =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

         return config, inputs_dict


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	List[str]   ,		*__lowerCAmelCase				:	List[Any])      ->					int:
         lowercase_     =      self.num_labels
         lowercase_     =      CTRLForSequenceClassification(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size)
         lowercase_     =      model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels))





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   lowerCamelCase_    					=(CTRLLMHeadModel,) if is_torch_available() else ()
   lowerCamelCase_    					=(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_    					=True
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	int)      ->					Optional[int]:
         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
               # config could not be created.
               return True

         return False


   def __UpperCAmelCase       (   self				:	str)      ->					Tuple:
         lowercase_     =      CTRLModelTester(self)
         lowercase_     =      ConfigTester(self   ,		config_class=__lowerCAmelCase   ,		n_embd=37)


   def __UpperCAmelCase       (   self				:	Dict)      ->					Optional[Any]:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase       (   self				:	str)      ->					List[Any]:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         pass


   @slow
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     =      CTRLModel.from_pretrained(__lowerCAmelCase)
               self.assertIsNotNone(__lowerCAmelCase)


   @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Dict:
         pass





@require_torch
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Any:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Any:
         lowercase_     =      CTRLLMHeadModel.from_pretrained("ctrl")
         model.to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(
             [[1_1859, 0, 1611, 8]]   ,		dtype=torch.long   ,		device=__lowerCAmelCase)  # Legal the president is
         lowercase_     =      [
             1_1859,
             0,
             1611,
             8,
             5,
             150,
             2_6449,
             2,
             19,
             348,
             469,
             3,
             2595,
             48,
             2_0740,
             24_6533,
             24_6533,
             19,
             30,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         lowercase_     =      model.generate(__lowerCAmelCase   ,		do_sample=__lowerCAmelCase)
         self.assertListEqual(output_ids[0].tolist()   ,		__lowerCAmelCase)






