
'''simple docstring'''






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


A			      =      logging.getLogger(__name__)




@dataclass
class     __snake_case		:
					_lowerCAmelCase			     =	42
					_lowerCAmelCase			     =	42
					_lowerCAmelCase			     =	42





@dataclass
class     __snake_case		:
					_lowerCAmelCase			     =	42
					_lowerCAmelCase			     =	42
					_lowerCAmelCase			     =	None
					_lowerCAmelCase			     =	None





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''train'''
					_lowerCAmelCase			     =	'''dev'''
					_lowerCAmelCase			     =	'''test'''





class     __snake_case		:
					@staticmethod
					def     UpperCAmelCase_							(     A,    A      ):

									"""simple docstring"""


									raise NotImplementedError
					@staticmethod
					def     UpperCAmelCase_							(     A      ):

									"""simple docstring"""


									raise NotImplementedError




					@staticmethod
					def     UpperCAmelCase_							(     A,    A,    A,    A,    A=False,    A="[CLS]",    A=1,    A="[SEP]",    A=False,    A=False,    A=0,    A=0,    A=-100,    A=0,    A=True,    ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= {label: i for i, label in enumerate(A      )}

									lowerCamelCase					:			Optional[int]									= []
									for ex_index, example in enumerate(A      ):
													if ex_index % 1_0000 == 0:
																	logger.info('Writing example %d of %d',    A,    len(A      )      )

													lowerCamelCase					:			int									= []
													lowerCamelCase					:			str									= []
													for word, label in zip(example.words,    example.labels      ):
																	lowerCamelCase					:			Any									= tokenizer.tokenize(A      )

																	# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																	if len(A      ) > 0:
																					tokens.extend(A      )
																					# 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(A      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
													lowerCamelCase					:			List[str]									= tokenizer.num_special_tokens_to_add()
													if len(A      ) > max_seq_length - special_tokens_count:
																	lowerCamelCase					:			int									= tokens[: (max_seq_length - special_tokens_count)]
																	lowerCamelCase					:			Tuple									= 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					:			Any									= [sequence_a_segment_id] * len(A      )

													if cls_token_at_end:
																	tokens += [cls_token]
																	label_ids += [pad_token_label_id]
																	segment_ids += [cls_token_segment_id]
													else:
																	lowerCamelCase					:			Dict									= [cls_token] + tokens
																	lowerCamelCase					:			List[Any]									= [pad_token_label_id] + label_ids
																	lowerCamelCase					:			Any									= [cls_token_segment_id] + segment_ids

													lowerCamelCase					:			Optional[int]									= tokenizer.convert_tokens_to_ids(A      )

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

													# Zero-pad up to the sequence length.
													lowerCamelCase					:			Optional[Any]									= max_seq_length - len(A      )
													if pad_on_left:
																	lowerCamelCase					:			Optional[int]									= ([pad_token] * padding_length) + input_ids
																	lowerCamelCase					:			Dict									= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																	lowerCamelCase					:			Any									= ([pad_token_segment_id] * padding_length) + segment_ids
																	lowerCamelCase					:			Optional[int]									= ([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(A      ) == max_seq_length
													assert len(A      ) == max_seq_length
													assert len(A      ) == max_seq_length
													assert len(A      ) == max_seq_length

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

													if "token_type_ids" not in tokenizer.model_input_names:
																	lowerCamelCase					:			Any									= None

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


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




					class     __snake_case		(   a__):
										_lowerCAmelCase			     =	42
										_lowerCAmelCase			     =	nn.CrossEntropyLoss().ignore_index
										def __init__(     self,    A,    A,    A,    A,    A,    A = None,    A=False,    A = Split.train,    ):

														"""simple docstring"""


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

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														lowerCamelCase					:			str									= cached_features_file + '.lock'
														with FileLock(A      ):
																		if os.path.exists(A      ) and not overwrite_cache:
																						logger.info(F'''Loading features from cached file {cached_features_file}'''      )
																						lowerCamelCase					:			Optional[Any]									= torch.load(A      )
																		else:
																						logger.info(F'''Creating features from dataset file at {data_dir}'''      )
																						lowerCamelCase					:			Dict									= token_classification_task.read_examples_from_file(A,    A      )
																						# TODO clean up all this to leverage built-in features of tokenizers
																						lowerCamelCase					:			str									= token_classification_task.convert_examples_to_features(
																						    A,    A,    A,    A,    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=A,    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,    A      )
										def __len__(     self      ):

														"""simple docstring"""


														return len(self.features      )




										def __getitem__(     self,    A      ):

														"""simple docstring"""


														return self.features[i]


if is_tf_available():
					import tensorflow as tf




					class     __snake_case		:
										_lowerCAmelCase			     =	42
										_lowerCAmelCase			     =	-100
										def __init__(     self,    A,    A,    A,    A,    A,    A = None,    A=False,    A = Split.train,    ):

														"""simple docstring"""


														lowerCamelCase					:			Optional[int]									= token_classification_task.read_examples_from_file(A,    A      )
														# TODO clean up all this to leverage built-in features of tokenizers
														lowerCamelCase					:			str									= token_classification_task.convert_examples_to_features(
														    A,    A,    A,    A,    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=A,    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					:			Any									= tf.data.Dataset.from_generator(
																		    A,    ({'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					:			List[str]									= tf.data.Dataset.from_generator(
																		    A,    ({'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     UpperCAmelCase_							(     self      ):

														"""simple docstring"""


														lowerCamelCase					:			Dict									= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

														return self.dataset
										def __len__(     self      ):

														"""simple docstring"""


														return len(self.features      )




										def __getitem__(     self,    A      ):

														"""simple docstring"""


														return self.features[i]


'''simple docstring'''






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers import AutoModelForCausalLM




@require_torch
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Optional[int]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= -1

									lowerCamelCase					:			Optional[Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			List[Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= tokenizer.decode(greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= TextStreamer(A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			List[Any]									= -1

									lowerCamelCase					:			Optional[int]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Any									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.decode(greedy_ids[0]      )

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

									lowerCamelCase					:			Union[str, Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= greedy_ids[:, input_ids.shape[1] :]
									lowerCamelCase					:			Dict									= tokenizer.decode(new_greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= TextStreamer(A,    skip_prompt=A      )
													model.generate(A,    max_new_tokens=10,    do_sample=A,    streamer=A      )
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

									lowerCamelCase					:			Any									= torch.ones((1, 5),    device=A      ).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
													lowerCamelCase					:			int									= TextStreamer(A,    skip_special_tokens=A      )
													model.generate(A,    max_new_tokens=1,    do_sample=A,    streamer=A      )

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									lowerCamelCase					:			List[str]									= cs.out[:-1]  # Remove the final "\n"
									lowerCamelCase					:			Optional[Any]									= tokenizer(A,    return_tensors='pt'      )
									self.assertEqual(streamer_text_tokenized.input_ids.shape,    (1, 1)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			List[str]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			Optional[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(A      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
