




"""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       SCREAMING_SNAKE_CASE__					:
						_a    						=						42
						_a    						=						42
						_a    						=						42






@dataclass
class       SCREAMING_SNAKE_CASE__					:
						_a    						=						42
						_a    						=						42
						_a    						=						None
						_a    						=						None






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'train'
						_a    						=						'dev'
						_a    						=						'test'






class       SCREAMING_SNAKE_CASE__					:


						@staticmethod
						def   __lowercase (							lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Union[Split, str]					):
										raise NotImplementedError


						@staticmethod
						def   __lowercase (							lowerCAmelCase				:			str					):
										raise NotImplementedError




						@staticmethod
						def   __lowercase (							lowerCAmelCase				:			List[InputExample]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			PreTrainedTokenizer						,						lowerCAmelCase				:			Optional[int]=False						,						lowerCAmelCase				:			Optional[Any]="[CLS]"						,						lowerCAmelCase				:			Any=1						,						lowerCAmelCase				:			Any="[SEP]"						,						lowerCAmelCase				:			Optional[int]=False						,						lowerCAmelCase				:			Optional[int]=False						,						lowerCAmelCase				:			Optional[int]=0						,						lowerCAmelCase				:			Optional[int]=0						,						lowerCAmelCase				:			int=-100						,						lowerCAmelCase				:			str=0						,						lowerCAmelCase				:			Dict=True						,						):
										lowerCAmelCase  =				{label: i for i, label in enumerate(lowerCAmelCase					)}

										lowerCAmelCase  =				[]
										for ex_index, example in enumerate(lowerCAmelCase					):
														if ex_index % 1_0000 == 0:
																		logger.info("""Writing example %d of %d"""						,						lowerCAmelCase						,						len(lowerCAmelCase					)					)

														lowerCAmelCase  =				[]
														lowerCAmelCase  =				[]
														for word, label in zip(example.words						,						example.labels					):
																		lowerCAmelCase  =				tokenizer.tokenize(lowerCAmelCase					)

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

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

														if cls_token_at_end:
																		tokens += [cls_token]
																		label_ids += [pad_token_label_id]
																		segment_ids += [cls_token_segment_id]
														else:
																		lowerCAmelCase  =				[cls_token] + tokens
																		lowerCAmelCase  =				[pad_token_label_id] + label_ids
																		lowerCAmelCase  =				[cls_token_segment_id] + segment_ids

														lowerCAmelCase  =				tokenizer.convert_tokens_to_ids(lowerCAmelCase					)

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

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

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

														if "token_type_ids" not in tokenizer.model_input_names:
																		lowerCAmelCase  =				None

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


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






		class       SCREAMING_SNAKE_CASE__					(					_a					):
								_a    						=						42
								_a    						=						nn.CrossEntropyLoss().ignore_index


								def __init__(							self				:			str						,						lowerCAmelCase				:			TokenClassificationTask						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			PreTrainedTokenizer						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[Any]=False						,						lowerCAmelCase				:			Split = Split.train						,						):
												# Load data features from cache or dataset file
												lowerCAmelCase  =				os.path.join(
												    lowerCAmelCase						,						"""cached_{}_{}_{}""".format(mode.value						,						tokenizer.__class__.__name__						,						str(lowerCAmelCase					)					)						,						)

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


								def __len__(							self				:			Dict					):
												return len(self.features					)




								def __getitem__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Tuple					):
												return self.features[i]


if is_tf_available():
		import tensorflow as tf






		class       SCREAMING_SNAKE_CASE__					:
								_a    						=						42
								_a    						=						-1_00


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

												return self.dataset


								def __len__(							self				:			List[str]					):
												return len(self.features					)




								def __getitem__(							self				:			List[Any]						,						lowerCAmelCase				:			Optional[int]					):
												return self.features[i]











"""simple docstring"""







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=lowerCAmelCase						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.out_channels						,						n_heads=self.num_attention_heads						,						d_head=self.out_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						only_cross_attention=self.only_cross_attention						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

										if self.add_downsample:
														lowerCAmelCase  =				FlaxDownsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			int=True					):
										lowerCAmelCase  =				()

										for resnet, attn in zip(self.resnets						,						self.attentions					):
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														output_states += (hidden_states,)

										if self.add_downsample:
														lowerCAmelCase  =				self.downsamplers_a(lowerCAmelCase					)
														output_states += (hidden_states,)

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=lowerCAmelCase						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)
										lowerCAmelCase  =				resnets

										if self.add_downsample:
														lowerCAmelCase  =				FlaxDownsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			int						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			List[str]=True					):
										lowerCAmelCase  =				()

										for resnet in self.resnets:
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														output_states += (hidden_states,)

										if self.add_downsample:
														lowerCAmelCase  =				self.downsamplers_a(lowerCAmelCase					)
														output_states += (hidden_states,)

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if (i == self.num_layers - 1) else self.out_channels
														lowerCAmelCase  =				self.prev_output_channel if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.out_channels						,						n_heads=self.num_attention_heads						,						d_head=self.out_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						only_cross_attention=self.only_cross_attention						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

										if self.add_upsample:
														lowerCAmelCase  =				FlaxUpsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Dict=True					):
										for resnet, attn in zip(self.resnets						,						self.attentions					):
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if (i == self.num_layers - 1) else self.out_channels
														lowerCAmelCase  =				self.prev_output_channel if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets

										if self.add_upsample:
														lowerCAmelCase  =				FlaxUpsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Dict=True					):
										for resnet in self.resnets:
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[Any]					):
										# there is always at least one resnet
										lowerCAmelCase  =				[
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels						,						out_channels=self.in_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
										]

										lowerCAmelCase  =				[]

										for _ in range(self.num_layers					):
														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.in_channels						,						n_heads=self.num_attention_heads						,						d_head=self.in_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=self.in_channels						,						out_channels=self.in_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions




						def __call__(							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]=True					):
										lowerCAmelCase  =				self.resnets[0](lowerCAmelCase						,						lowerCAmelCase					)
										for attn, resnet in zip(self.attentions						,						self.resnets[1:]					):
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										return hidden_states





