

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_							=			logging.getLogger(__name__)
@dataclass
class        snake_case_		:
		lowerCamelCase    :str
		lowerCamelCase    :List[str]
		lowerCamelCase    :Optional[List[str]]

@dataclass
class        snake_case_		:
		lowerCamelCase    :List[int]
		lowerCamelCase    :List[int]
		lowerCamelCase    :Optional[List[int]]						=     None
		lowerCamelCase    :Optional[List[int]]						=     None

class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     "train"
		lowerCamelCase    :List[Any]						=     "dev"
		lowerCamelCase    :Optional[int]						=     "test"

class        snake_case_		:







		@staticmethod
		def 			__lowercase   (		__lowercase			,     __lowercase   )				->   List[InputExample]:
				raise NotImplementedError







		@staticmethod
		def 			__lowercase   (		__lowercase   )				->   List[str]:
				raise NotImplementedError







		@staticmethod
		def 			__lowercase   (		__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase=False			,     __lowercase="[CLS]"			,     __lowercase=1			,     __lowercase="[SEP]"			,     __lowercase=False			,     __lowercase=False			,     __lowercase=0			,     __lowercase=0			,     __lowercase=-1_0_0			,     __lowercase=0			,     __lowercase=True			,     )				->   List[InputFeatures]:
				lowerCamelCase   : List[Any]								={label: i for i, label in enumerate(__lowercase   )}

				lowerCamelCase   : List[str]								=[]
				for ex_index, example in enumerate(__lowercase   ):
						if ex_index % 1_0_0_0_0 == 0:
								logger.info('''Writing example %d of %d'''			,     __lowercase			,     len(__lowercase   )   )

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

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

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

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

						lowerCamelCase   : Dict								=tokenizer.convert_tokens_to_ids(__lowercase   )

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

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

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

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

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


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset
				class        snake_case_		(			_A):
						lowerCamelCase    :List[InputFeatures]
						lowerCamelCase    :int						=     nn.CrossEntropyLoss().ignore_index







						def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase = None			,     __lowercase=False			,     __lowercase = Split.train			,     )				->   List[Any]:
								# Load data features from cache or dataset file
								lowerCamelCase   : Tuple								=os.path.join(
								    __lowercase			,     '''cached_{}_{}_{}'''.format(mode.value			,     tokenizer.__class__.__name__			,     str(__lowercase   )   )			,     )

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







						def __len__(		self   )				->   Optional[int]:
								return len(self.features   )







						def __getitem__(		self			,     __lowercase   )				->   InputFeatures:
								return self.features[i]


if is_tf_available():
				import tensorflow as tf
				class        snake_case_		:
						lowerCamelCase    :List[InputFeatures]
						lowerCamelCase    :int						=     -100







						def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase = None			,     __lowercase=False			,     __lowercase = Split.train			,     )				->   Optional[Any]:
								lowerCamelCase   : Optional[Any]								=token_classification_task.read_examples_from_file(__lowercase			,     __lowercase   )
								# TODO clean up all this to leverage built-in features of tokenizers
								lowerCamelCase   : str								=token_classification_task.convert_examples_to_features(
								    __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     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=__lowercase			,     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   : int								=tf.data.Dataset.from_generator(
										    __lowercase			,     ({'''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[Any]								=tf.data.Dataset.from_generator(
										    __lowercase			,     ({'''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 			__lowercase   (		self   )				->   Union[str, Any]:
								lowerCamelCase   : int								=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

								return self.dataset







						def __len__(		self   )				->   Optional[int]:
								return len(self.features   )







						def __getitem__(		self			,     __lowercase   )				->   InputFeatures:
								return self.features[i]








# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


snake_case_							=			'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
				import torch

				print('''Torch version:''', torch.__version__)
				print('''Cuda available:''', torch.cuda.is_available())
				print('''Cuda version:''', torch.version.cuda)
				print('''CuDNN version:''', torch.backends.cudnn.version())
				print('''Number of GPUs available:''', torch.cuda.device_count())
				print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
				print('''Torch version:''', None)

try:
				import deepspeed

				print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
				print('''DeepSpeed version:''', None)

try:
				import tensorflow as tf

				print('''TensorFlow version:''', tf.__version__)
				print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
				print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
				print('''TensorFlow version:''', None)

