


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					seq_length
										SCREAMING_SNAKE_CASE_		:     List[str]					=					is_training
										SCREAMING_SNAKE_CASE_		:     Any					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     Tuple					=					use_labels
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     int					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     str					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Dict					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					None
										if self.use_attention_mask:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

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

										SCREAMING_SNAKE_CASE_		:     int					=					RobertaPreLayerNormConfig(
										    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=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Dict					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					True
										SCREAMING_SNAKE_CASE_		:     Any					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

							_UpperCAmelCase      =						(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					FlaxRobertaPreLayerNormModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )







@require_flax
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					model(lowerCAmelCase__       )[0]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[1, 1_1, 5_0_2_6_5]
										self.assertEqual(list(output.shape       )			,						lowerCAmelCase__       )
										# compare the actual values for a slice.
										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array(
										    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]			,						dtype=np.floataa       )
										self.assertTrue(np.allclose(output[:, :3, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,						from_pt=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
										SCREAMING_SNAKE_CASE_		:     Any					=					model(lowerCAmelCase__       )[0]
										# compare the actual values for a slice.
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array(
										    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]			,						dtype=np.floataa       )
										self.assertTrue(np.allclose(output[:, :3, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )









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__			(				A__,      A__,      A__,      A__,      A__=True,      A__="pt"				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					{'add_prefix_space': True} if isinstance(A__,      A__				) and not line.startswith(' '				) else {}
			SCREAMING_SNAKE_CASE_		:     Dict					=					padding_side
			return tokenizer(
			    [line],      max_length=A__,      padding='max_length' if pad_to_max_length else None,      truncation=A__,      return_tensors=A__,      add_special_tokens=A__,      **A__,      )






def 						a__			(				A__,      A__,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					input_ids.ne(A__				).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    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="train"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=""			,						):





										"""simple docstring"""




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






							def __len__(      self       ):





										"""simple docstring"""




										return len(self.src_lens       )






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prefix + linecache.getline(str(self.src_file       )			,						lowerCAmelCase__       ).rstrip('\n'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					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
										SCREAMING_SNAKE_CASE_		:     Any					=					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer.generator if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer

										SCREAMING_SNAKE_CASE_		:     Dict					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_source_length			,						'right'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_target_length			,						'right'       )

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






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




										return [len(lowerCAmelCase__       ) for x in Path(lowerCAmelCase__       ).open().readlines()]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['attention_mask'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.stack([x['decoder_input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__			,						attention_mask=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{
										    'input_ids': source_ids,
										    'attention_mask': source_mask,
										    'decoder_input_ids': y,
										}
										return batch







lowerCAmelCase__			:      Tuple								=getLogger(__name__)






def 						a__			(				A__				):
			return list(itertools.chain.from_iterable(A__				)				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_git_info()
			save_json(A__,      os.path.join(A__,      'git_log.json'				)				)






def 						a__			(				A__,      A__,      A__=4,      **A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__,      indent=A__,      **A__				)






def 						a__			(				A__				):
			with open(A__				) as f:
						return json.load(A__				)






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






def 						a__			(				A__,      A__				):
			return list(map(A__,      A__				)				)






def 						a__			(				A__,      A__				):
			with open(A__,      'wb'				) as f:
						return pickle.dump(A__,      A__				)






def 						a__			(				A__				):

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

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

			def remove_punc(A__				):
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					set(string.punctuation				)
						return "".join(ch for ch in text if ch not in exclude				)

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

			return white_space_fix(remove_articles(remove_punc(lower(A__				)				)				)				)






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






def 						a__			(				A__,      A__				):
			return normalize_answer(A__				) == normalize_answer(A__				)






def 						a__			(				A__,      A__				):
			assert len(A__				) == len(A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					0
			for hypo, pred in zip(A__,      A__				):
						em += exact_match_score(A__,      A__				)
			if len(A__				) > 0:
						em /= len(A__				)
			return {"em": em}






def 						a__			(				A__				):
			return model_prefix.startswith('rag'				)






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





