





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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=True							,     _SCREAMING_SNAKE_CASE="pt"				)							-> List[Any]:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None							,     )							-> Tuple:




    """simple docstring"""




    _A     =							input_ids.ne(_SCREAMING_SNAKE_CASE				).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      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


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




     def __len__(    self							):
         return len(self.src_lens							)




     def __getitem__(    self				,     snake_case_							):
         _A     =							index + 1  # linecache starts at 1
         _A     =							self.prefix + linecache.getline(str(self.src_file							)				,     snake_case_							).rstrip('\n'							)
         _A     =							linecache.getline(str(self.tgt_file							)				,     snake_case_							).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				,     snake_case_							):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

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

         _A     =							encode_line(snake_case_				,     snake_case_				,     self.max_source_length				,     'right'							)
         _A     =							encode_line(snake_case_				,     snake_case_				,     self.max_target_length				,     'right'							)

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




     @staticmethod
     def        lowerCAmelCase__		(    snake_case_							):
         return [len(snake_case_							) for x in Path(snake_case_							).open().readlines()]




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							torch.stack([x['input_ids'] for x in batch]							)
         _A     =							torch.stack([x['attention_mask'] for x in batch]							)
         _A     =							torch.stack([x['decoder_input_ids'] for x in batch]							)
         _A     =							(
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer				,     snake_case_							)
             else self.tokenizer.pad_token_id
         )
         _A     =							(
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer				,     snake_case_							)
             else self.tokenizer.pad_token_id
         )
         _A     =							trim_batch(snake_case_				,     snake_case_							)
         _A,			_A     =							trim_batch(snake_case_				,     snake_case_				,     attention_mask=snake_case_							)
         _A     =							{
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> None:




    """simple docstring"""




    _A     =							get_git_info()
    save_json(_SCREAMING_SNAKE_CASE							,     os.path.join(_SCREAMING_SNAKE_CASE							,     'git_log.json'				)				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=4							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        json.dump(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     indent=_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




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





def 				__lowerCAmelCase(       )							-> Union[str, Any]:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List:




    """simple docstring"""




    return list(map(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    with open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        return pickle.dump(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    def remove_articles(_SCREAMING_SNAKE_CASE				):
        return re.sub(R'\b(a|an|the)\b'							,     ' '							,     _SCREAMING_SNAKE_CASE				)

    def white_space_fix(_SCREAMING_SNAKE_CASE				):
        return " ".join(text.split()				)

    def remove_punc(_SCREAMING_SNAKE_CASE				):
        _A     =							set(string.punctuation				)
        return "".join(ch for ch in text if ch not in exclude				)

    def lower(_SCREAMING_SNAKE_CASE				):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE				)				)				)				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    return normalize_answer(_SCREAMING_SNAKE_CASE				) == normalize_answer(_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    return model_prefix.startswith('rag'				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




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












import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 (
     NystromformerForMaskedLM,
     NystromformerForMultipleChoice,
     NystromformerForQuestionAnswering,
     NystromformerForSequenceClassification,
     NystromformerForTokenClassification,
     NystromformerModel,
 )
 from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=7				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=99				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=16				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=4				,     snake_case_=None				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							seq_length
         _A     =							is_training
         _A     =							use_input_mask
         _A     =							use_token_type_ids
         _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     =							type_vocab_size
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							num_labels
         _A     =							num_choices
         _A     =							scope




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

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

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

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

         _A     =							self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         return NystromformerConfig(
             vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     is_decoder=snake_case_				,     initializer_range=self.initializer_range				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForMaskedLM(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForQuestionAnswering(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     start_positions=snake_case_				,     end_positions=snake_case_				,     )
         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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForSequenceClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForTokenClassification(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_choices
         _A     =							NystromformerForMultipleChoice(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							input_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							token_type_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							input_mask.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_				,     )
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': NystromformerModel,
             'fill-mask': NystromformerForMaskedLM,
             'question-answering': NystromformerForQuestionAnswering,
             'text-classification': NystromformerForSequenceClassification,
             'token-classification': NystromformerForTokenClassification,
             'zero-shot': NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             _A     =							type
             self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							NystromformerModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)







@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModel.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							torch.tensor([[0, 1, 2, 3, 4, 5]]							)

         with torch.no_grad():
             _A     =							model(snake_case_							)[0]

         _A     =							torch.Size((1, 6, 768)							)
         self.assertEqual(output.shape				,     snake_case_							)

         _A     =							torch.tensor(
             [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]							)

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




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							'the [MASK] of Belgium is Brussels'

         _A     =							AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'							)

         _A     =							tokenizer(snake_case_				,     return_tensors='pt'							)

         with torch.no_grad():
             _A     =							model(encoding.input_ids							).logits

         _A     =							token_logits[:, 2, :].argmax(-1							)[0]

         self.assertEqual(tokenizer.decode(snake_case_							)				,     'capital'							)





