



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_   (  _a    :		Any					,						_a    :		Tuple					,						_a    :		Any					,						_a    :		Any					,						_a    :		Optional[Any]=True					,						_a    :		Tuple="pt"  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Optional[Any]   						= {'''add_prefix_space''': True} if isinstance(_lowercase					,						_lowercase  ) and not line.startswith(""" """  ) else {}
    UpperCAmelCase_			:    str   						= padding_side
    return tokenizer(
        [line]					,						max_length=_lowercase					,						padding="""max_length""" if pad_to_max_length else None					,						truncation=_lowercase					,						return_tensors=_lowercase					,						add_special_tokens=_lowercase					,						**_lowercase					,						)







def 	lowerCamelCase_   (  _a    :		Tuple					,						_a    :		Dict					,						_a    :		Dict=None					,						):



    '''simple docstring'''




    UpperCAmelCase_			:    Dict   						= input_ids.ne(_lowercase  ).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 	_snake_case							(			UpperCamelCase_      ):






   '''simple docstring'''






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


   def __len__(  self:			Union[str, Any]				)     ->	Any:
       return len(self.src_lens				)


   def __getitem__(  self:			List[str]   ,lowerCamelCase_:			Union[str, Any]				)     ->	Tuple:
       UpperCAmelCase_			:    List[Any]   						= index + 1  # linecache starts at 1
       UpperCAmelCase_			:    List[Any]   						= self.prefix + linecache.getline(str(self.src_file				)   ,UpperCamelCase__				).rstrip("""\n"""				)
       UpperCAmelCase_			:    Dict   						= linecache.getline(str(self.tgt_file				)   ,UpperCamelCase__				).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   ,UpperCamelCase__				):
           source_line += self.tokenizer.eos_token
           tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       UpperCAmelCase_			:    str   						= (
           self.tokenizer.question_encoder if isinstance(self.tokenizer   ,UpperCamelCase__				) else self.tokenizer
       )
       UpperCAmelCase_			:    Dict   						= self.tokenizer.generator if isinstance(self.tokenizer   ,UpperCamelCase__				) else self.tokenizer

       UpperCAmelCase_			:    List[str]   						= encode_line(UpperCamelCase__   ,UpperCamelCase__   ,self.max_source_length   ,"""right"""				)
       UpperCAmelCase_			:    List[str]   						= encode_line(UpperCamelCase__   ,UpperCamelCase__   ,self.max_target_length   ,"""right"""				)

       UpperCAmelCase_			:    Optional[int]   						= source_inputs['''input_ids'''].squeeze()
       UpperCAmelCase_			:    Dict   						= target_inputs['''input_ids'''].squeeze()
       UpperCAmelCase_			:    List[str]   						= source_inputs['''attention_mask'''].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }


   @staticmethod
   def 	A__  (  lowerCamelCase_:			Any				)     ->	Dict:
       return [len(UpperCamelCase__				) for x in Path(UpperCamelCase__				).open().readlines()]






   def 	A__  (  self:			Optional[Any]   ,lowerCamelCase_:			Optional[Any]				)     ->	Dict:
       UpperCAmelCase_			:    List[str]   						= torch.stack([x["""input_ids"""] for x in batch]				)
       UpperCAmelCase_			:    Optional[Any]   						= torch.stack([x["""attention_mask"""] for x in batch]				)
       UpperCAmelCase_			:    List[Any]   						= torch.stack([x["""decoder_input_ids"""] for x in batch]				)
       UpperCAmelCase_			:    Optional[int]   						= (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer   ,UpperCamelCase__				)
           else self.tokenizer.pad_token_id
       )
       UpperCAmelCase_			:    Any   						= (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer   ,UpperCamelCase__				)
           else self.tokenizer.pad_token_id
       )
       UpperCAmelCase_			:    Optional[int]   						= trim_batch(UpperCamelCase__   ,UpperCamelCase__				)
       UpperCAmelCase_			:    str   						= trim_batch(UpperCamelCase__   ,UpperCamelCase__   ,attention_mask=UpperCamelCase__				)
       UpperCAmelCase_			:    Dict   						= {
           '''input_ids''': source_ids,
           '''attention_mask''': source_mask,
           '''decoder_input_ids''': y,
       }
       return batch


UpperCamelCase_										=					getLogger(__name__)







def 	lowerCamelCase_   (  _a    :		List[str]  ):



    '''simple docstring'''




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







def 	lowerCamelCase_   (  _a    :		int  ):



    '''simple docstring'''




    UpperCAmelCase_			:    List[Any]   						= get_git_info()
    save_json(_lowercase					,						os.path.join(_lowercase					,						"""git_log.json"""  )  )







def 	lowerCamelCase_   (  _a    :		Optional[Any]					,						_a    :		Any					,						_a    :		Union[str, Any]=4					,						**_a    :		Tuple  ):



    '''simple docstring'''




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







def 	lowerCamelCase_   (  _a    :		int  ):



    '''simple docstring'''




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







def 	lowerCamelCase_   (  ):



    '''simple docstring'''




    UpperCAmelCase_			:    List[str]   						= git.Repo(search_parent_directories=_lowercase  )
    UpperCAmelCase_			:    Union[str, Any]   						= {
        '''repo_id''': str(_lowercase  ),
        '''repo_sha''': str(repo.head.object.hexsha  ),
        '''repo_branch''': str(repo.active_branch  ),
        '''hostname''': str(socket.gethostname()  ),
    }
    return repo_infos







def 	lowerCamelCase_   (  _a    :		Tuple					,						_a    :		Optional[Any]  ):



    '''simple docstring'''




    return list(map(_lowercase					,						_lowercase  )  )







def 	lowerCamelCase_   (  _a    :		Optional[Any]					,						_a    :		List[str]  ):



    '''simple docstring'''




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







def 	lowerCamelCase_   (  _a    :		List[str]  ):



    '''simple docstring'''




    def remove_articles(_a    :		Optional[int]  ):
        return re.sub(r"""\b(a|an|the)\b"""					,						""" """					,						_lowercase  )

    def white_space_fix(_a    :		int  ):
        return " ".join(text.split()  )

    def remove_punc(_a    :		List[Any]  ):
        UpperCAmelCase_			:    int   						= set(string.punctuation  )
        return "".join(ch for ch in text if ch not in exclude  )

    def lower(_a    :		str  ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_lowercase  )  )  )  )







def 	lowerCamelCase_   (  _a    :		List[Any]					,						_a    :		List[Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Tuple   						= normalize_answer(_lowercase  ).split()
    UpperCAmelCase_			:    Union[str, Any]   						= normalize_answer(_lowercase  ).split()
    UpperCAmelCase_			:    List[str]   						= Counter(_lowercase  ) & Counter(_lowercase  )
    UpperCAmelCase_			:    Dict   						= sum(common.values()  )
    if num_same == 0:
        return 0
    UpperCAmelCase_			:    int   						= 1.0 * num_same / len(_lowercase  )
    UpperCAmelCase_			:    Optional[Any]   						= 1.0 * num_same / len(_lowercase  )
    UpperCAmelCase_			:    Tuple   						= (2 * precision * recall) / (precision + recall)
    return fa







def 	lowerCamelCase_   (  _a    :		List[str]					,						_a    :		str  ):



    '''simple docstring'''




    return normalize_answer(_lowercase  ) == normalize_answer(_lowercase  )







def 	lowerCamelCase_   (  _a    :		Optional[int]					,						_a    :		Optional[Any]  ):



    '''simple docstring'''




    assert len(_lowercase  ) == len(_lowercase  )
    UpperCAmelCase_			:    Union[str, Any]   						= 0
    for hypo, pred in zip(_lowercase					,						_lowercase  ):
        em += exact_match_score(_lowercase					,						_lowercase  )
    if len(_lowercase  ) > 0:
        em /= len(_lowercase  )
    return {"em": em}







def 	lowerCamelCase_   (  _a    :		int  ):



    '''simple docstring'''




    return model_prefix.startswith("""rag"""  )







def 	lowerCamelCase_   (  _a    :		Tuple					,						_a    :		str					,						_a    :		Union[str, Any]  ):



    '''simple docstring'''




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

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												for model_result in results.values():
																		for batch_size, sequence_length in zip(model_result['''bs''']    ,		model_result['''ss''']						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_result['''result'''][batch_size][sequence_length]
																								self.assertIsNotNone(UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sgugger/tiny-distilbert-classification'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		only_pretrain_model=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    str							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''patrickvonplaten/t5-tiny-random'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		configs=[config]						)
												SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''						)						) == 0    ,		'''Cannot do xla on CPU.'''						)
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		use_xla=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		save_to_csv=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		inference_time_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)    ,		inference_memory_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)    ,		env_info_csv_file=os.path.join(UpperCamelCase__    ,		'''env.csv'''						)    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		benchmark.run()
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''env.csv'''						)						).exists()						)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(UpperCamelCase__	:				Dict						):
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''sequential'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''cumulative'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''current'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''total'''						)						)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		log_filename=os.path.join(UpperCamelCase__    ,		'''log.txt'''						)    ,		log_print=UpperCamelCase__    ,		trace_memory_line_by_line=UpperCamelCase__    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
																		_check_summary_is_not_empty(result.inference_summary						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''log.txt'''						)						).exists()						)





