



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__snake_case       :	List[Any]            =logging.getLogger(__name__)



@dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42
		snake_case_			=42



@dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42
		snake_case_			=None
		snake_case_			=None



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""train"""
		snake_case_			="""dev"""
		snake_case_			="""test"""



class  lowerCamelCase__    :




		'''simple docstring'''



		@staticmethod
		def 	lowerCAmelCase__   (__lowerCamelCase	,__lowerCamelCase				)  ->    List[InputExample]:

						"""simple docstring"""






						raise NotImplementedError



		@staticmethod
		def 	lowerCAmelCase__   (__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						raise NotImplementedError



		@staticmethod
		def 	lowerCAmelCase__   (__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=False	,__lowerCamelCase="[CLS]"	,__lowerCamelCase=1	,__lowerCamelCase="[SEP]"	,__lowerCamelCase=False	,__lowerCamelCase=False	,__lowerCamelCase=0	,__lowerCamelCase=0	,__lowerCamelCase=-1_00	,__lowerCamelCase=0	,__lowerCamelCase=True	,)  ->    List[InputFeatures]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE				)}

						lowerCAmelCase__  :       str				=  []
						for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE				):
										if ex_index % 1_00_00 == 0:
														logger.info('''Writing example %d of %d'''	,_SCREAMING_SNAKE_CASE	,len(_SCREAMING_SNAKE_CASE				)				)

										lowerCAmelCase__  :       Any				=  []
										lowerCAmelCase__  :       List[Any]				=  []
										for word, label in zip(example.words	,example.labels				):
														lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(_SCREAMING_SNAKE_CASE				) > 0:
																		tokens.extend(_SCREAMING_SNAKE_CASE				)
																		# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																		label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE				) - 1)				)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
										lowerCAmelCase__  :       Optional[int]				=  tokenizer.num_special_tokens_to_add()
										if len(_SCREAMING_SNAKE_CASE				) > max_seq_length - special_tokens_count:
														lowerCAmelCase__  :       Union[str, Any]				=  tokens[: (max_seq_length - special_tokens_count)]
														lowerCAmelCase__  :       Union[str, Any]				=  label_ids[: (max_seq_length - special_tokens_count)]

										# The convention in BERT is:
										# (a) For sequence pairs:
										#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
										#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
										# (b) For single sequences:
										#  tokens:   [CLS] the dog is hairy . [SEP]
										#  type_ids:   0   0   0   0  0     0   0
										#
										# Where "type_ids" are used to indicate whether this is the first
										# sequence or the second sequence. The embedding vectors for `type=0` and
										# `type=1` were learned during pre-training and are added to the wordpiece
										# embedding vector (and position vector). This is not *strictly* necessary
										# since the [SEP] token unambiguously separates the sequences, but it makes
										# it easier for the model to learn the concept of sequences.
										#
										# For classification tasks, the first vector (corresponding to [CLS]) is
										# used as the "sentence vector". Note that this only makes sense because
										# the entire model is fine-tuned.
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
										if sep_token_extra:
														# roberta uses an extra separator b/w pairs of sentences
														tokens += [sep_token]
														label_ids += [pad_token_label_id]
										lowerCAmelCase__  :       Dict				=  [sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE				)

										if cls_token_at_end:
														tokens += [cls_token]
														label_ids += [pad_token_label_id]
														segment_ids += [cls_token_segment_id]
										else:
														lowerCAmelCase__  :       Optional[int]				=  [cls_token] + tokens
														lowerCAmelCase__  :       Optional[int]				=  [pad_token_label_id] + label_ids
														lowerCAmelCase__  :       List[Any]				=  [cls_token_segment_id] + segment_ids

										lowerCAmelCase__  :       Optional[Any]				=  tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)

										# The mask has 1 for real tokens and 0 for padding tokens. Only real
										# tokens are attended to.
										lowerCAmelCase__  :       str				=  [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE				)

										# Zero-pad up to the sequence length.
										lowerCAmelCase__  :       Tuple				=  max_seq_length - len(_SCREAMING_SNAKE_CASE				)
										if pad_on_left:
														lowerCAmelCase__  :       List[Any]				=  ([pad_token] * padding_length) + input_ids
														lowerCAmelCase__  :       Union[str, Any]				=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
														lowerCAmelCase__  :       Union[str, Any]				=  ([pad_token_segment_id] * padding_length) + segment_ids
														lowerCAmelCase__  :       Any				=  ([pad_token_label_id] * padding_length) + label_ids
										else:
														input_ids += [pad_token] * padding_length
														input_mask += [0 if mask_padding_with_zero else 1] * padding_length
														segment_ids += [pad_token_segment_id] * padding_length
														label_ids += [pad_token_label_id] * padding_length

										assert len(_SCREAMING_SNAKE_CASE				) == max_seq_length
										assert len(_SCREAMING_SNAKE_CASE				) == max_seq_length
										assert len(_SCREAMING_SNAKE_CASE				) == max_seq_length
										assert len(_SCREAMING_SNAKE_CASE				) == max_seq_length

										if ex_index < 5:
														logger.info('''*** Example ***'''				)
														logger.info('''guid: %s'''	,example.guid				)
														logger.info('''tokens: %s'''	,''' '''.join([str(_SCREAMING_SNAKE_CASE				) for x in tokens]				)				)
														logger.info('''input_ids: %s'''	,''' '''.join([str(_SCREAMING_SNAKE_CASE				) for x in input_ids]				)				)
														logger.info('''input_mask: %s'''	,''' '''.join([str(_SCREAMING_SNAKE_CASE				) for x in input_mask]				)				)
														logger.info('''segment_ids: %s'''	,''' '''.join([str(_SCREAMING_SNAKE_CASE				) for x in segment_ids]				)				)
														logger.info('''label_ids: %s'''	,''' '''.join([str(_SCREAMING_SNAKE_CASE				) for x in label_ids]				)				)

										if "token_type_ids" not in tokenizer.model_input_names:
														lowerCAmelCase__  :       Tuple				=  None

										features.append(
										    InputFeatures(
										        input_ids=_SCREAMING_SNAKE_CASE	,attention_mask=_SCREAMING_SNAKE_CASE	,token_type_ids=_SCREAMING_SNAKE_CASE	,label_ids=_SCREAMING_SNAKE_CASE				)				)
						return features


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset



			class  lowerCamelCase__    (							lowerCamelCase__):




					'''simple docstring'''



					snake_case_			=42
					snake_case_			=nn.CrossEntropyLoss().ignore_index



					def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase=False	,__lowerCamelCase = Split.train	,)  ->    Optional[int]:

									"""simple docstring"""






									lowerCAmelCase__  :       List[Any]				=  os.path.join(
									    _SCREAMING_SNAKE_CASE	,'''cached_{}_{}_{}'''.format(mode.value	,tokenizer.__class__.__name__	,str(_SCREAMING_SNAKE_CASE				)				)	,)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									lowerCAmelCase__  :       Union[str, Any]				=  cached_features_file + '''.lock'''
									with FileLock(_SCREAMING_SNAKE_CASE				):
													if os.path.exists(_SCREAMING_SNAKE_CASE				) and not overwrite_cache:
																	logger.info(f"""Loading features from cached file {cached_features_file}"""				)
																	lowerCAmelCase__  :       Dict				=  torch.load(_SCREAMING_SNAKE_CASE				)
													else:
																	logger.info(f"""Creating features from dataset file at {data_dir}"""				)
																	lowerCAmelCase__  :       int				=  token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE				)
																	# TODO clean up all this to leverage built-in features of tokenizers
																	lowerCAmelCase__  :       int				=  token_classification_task.convert_examples_to_features(
																	    _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,cls_token_at_end=bool(model_type in ['''xlnet''']				)	,cls_token=tokenizer.cls_token	,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0	,sep_token=tokenizer.sep_token	,sep_token_extra=_SCREAMING_SNAKE_CASE	,pad_on_left=bool(tokenizer.padding_side == '''left'''				)	,pad_token=tokenizer.pad_token_id	,pad_token_segment_id=tokenizer.pad_token_type_id	,pad_token_label_id=self.pad_token_label_id	,)
																	logger.info(f"""Saving features into cached file {cached_features_file}"""				)
																	torch.save(self.features	,_SCREAMING_SNAKE_CASE				)



					def __len__(self				)  ->    List[Any]:

									"""simple docstring"""






									return len(self.features				)



					def __getitem__(self	,__lowerCamelCase				)  ->    InputFeatures:

									"""simple docstring"""






									return self.features[i]


if is_tf_available():
			import tensorflow as tf



			class  lowerCamelCase__    :




					'''simple docstring'''



					snake_case_			=42
					snake_case_			=-100



					def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase=False	,__lowerCamelCase = Split.train	,)  ->    str:

									"""simple docstring"""






									lowerCAmelCase__  :       Optional[Any]				=  token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE				)
									# TODO clean up all this to leverage built-in features of tokenizers
									lowerCAmelCase__  :       List[str]				=  token_classification_task.convert_examples_to_features(
									    _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,cls_token_at_end=bool(model_type in ['''xlnet''']				)	,cls_token=tokenizer.cls_token	,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0	,sep_token=tokenizer.sep_token	,sep_token_extra=_SCREAMING_SNAKE_CASE	,pad_on_left=bool(tokenizer.padding_side == '''left'''				)	,pad_token=tokenizer.pad_token_id	,pad_token_segment_id=tokenizer.pad_token_type_id	,pad_token_label_id=self.pad_token_label_id	,)

									def gen():
													for ex in self.features:
																	if ex.token_type_ids is None:
																					yield (
																					    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																					    ex.label_ids,
																					)
																	else:
																					yield (
																					    {
																					        "input_ids": ex.input_ids,
																					        "attention_mask": ex.attention_mask,
																					        "token_type_ids": ex.token_type_ids,
																					    },
																					    ex.label_ids,
																					)

									if "token_type_ids" not in tokenizer.model_input_names:
													lowerCAmelCase__  :       Optional[int]				=  tf.data.Dataset.from_generator(
													    _SCREAMING_SNAKE_CASE	,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)	,(
													        {'''input_ids''': tf.TensorShape([None]				), '''attention_mask''': tf.TensorShape([None]				)},
													        tf.TensorShape([None]				),
													    )	,)
									else:
													lowerCAmelCase__  :       Optional[int]				=  tf.data.Dataset.from_generator(
													    _SCREAMING_SNAKE_CASE	,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)	,(
													        {
													            '''input_ids''': tf.TensorShape([None]				),
													            '''attention_mask''': tf.TensorShape([None]				),
													            '''token_type_ids''': tf.TensorShape([None]				),
													        },
													        tf.TensorShape([None]				),
													    )	,)



					def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

									"""simple docstring"""






									lowerCAmelCase__  :       List[str]				=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features				)				)				)

									return self.dataset



					def __len__(self				)  ->    int:

									"""simple docstring"""






									return len(self.features				)



					def __getitem__(self	,__lowerCamelCase				)  ->    InputFeatures:

									"""simple docstring"""






									return self.features[i]






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

