


'''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 :  int    							=				logging.getLogger(__name__)






@dataclass
class        __snake_case	:







				"""simple docstring"""



				lowercase       			= 42
				lowercase       			= 42
				lowercase       			= 42







@dataclass
class        __snake_case	:







				"""simple docstring"""



				lowercase       			= 42
				lowercase       			= 42
				lowercase       			= None
				lowercase       			= None







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				lowercase       			= 'train'
				lowercase       			= 'dev'
				lowercase       			= 'test'







class        __snake_case	:







				"""simple docstring"""



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

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





				@staticmethod
				def __lowercase       (       lowerCamelCase   :  List[InputExample]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  PreTrainedTokenizer   ,						lowerCamelCase   :  List[str]=False   ,						lowerCamelCase   :  Any="[CLS]"   ,						lowerCamelCase   :  List[str]=1   ,						lowerCamelCase   :  List[str]="[SEP]"   ,						lowerCamelCase   :  Optional[int]=False   ,						lowerCamelCase   :  int=False   ,						lowerCamelCase   :  Tuple=0   ,						lowerCamelCase   :  List[str]=0   ,						lowerCamelCase   :  Tuple=-1_00   ,						lowerCamelCase   :  Dict=0   ,						lowerCamelCase   :  Union[str, Any]=True   ,						)       ->  List[InputFeatures]:
						lowerCAmelCase_      :				Dict      =       {label: i for i, label in enumerate(lowerCamelCase		)}

						lowerCAmelCase_      :				Dict      =       []
						for ex_index, example in enumerate(lowerCamelCase		):
								if ex_index % 1_00_00 == 0:
										logger.info("""Writing example %d of %d"""   ,						lowerCamelCase   ,						len(lowerCamelCase		)		)

								lowerCAmelCase_      :				List[str]      =       []
								lowerCAmelCase_      :				Union[str, Any]      =       []
								for word, label in zip(example.words   ,						example.labels		):
										lowerCAmelCase_      :				str      =       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_      :				Dict      =       tokenizer.num_special_tokens_to_add()
								if len(lowerCamelCase		) > max_seq_length - special_tokens_count:
										lowerCAmelCase_      :				int      =       tokens[: (max_seq_length - special_tokens_count)]
										lowerCAmelCase_      :				List[str]      =       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(lowerCamelCase		)

								if cls_token_at_end:
										tokens += [cls_token]
										label_ids += [pad_token_label_id]
										segment_ids += [cls_token_segment_id]
								else:
										lowerCAmelCase_      :				Union[str, Any]      =       [cls_token] + tokens
										lowerCAmelCase_      :				Union[str, Any]      =       [pad_token_label_id] + label_ids
										lowerCAmelCase_      :				int      =       [cls_token_segment_id] + segment_ids

								lowerCAmelCase_      :				Dict      =       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_      :				Optional[Any]      =       [1 if mask_padding_with_zero else 0] * len(lowerCamelCase		)

								# Zero-pad up to the sequence length.
								lowerCAmelCase_      :				Tuple      =       max_seq_length - len(lowerCamelCase		)
								if pad_on_left:
										lowerCAmelCase_      :				List[str]      =       ([pad_token] * padding_length) + input_ids
										lowerCAmelCase_      :				Tuple      =       ([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_      :				List[str]      =       ([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_      :				int      =       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        __snake_case	(       _SCREAMING_SNAKE_CASE):







						"""simple docstring"""



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

						def __init__(       self   :  List[Any]   ,						lowerCamelCase   :  TokenClassificationTask   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  PreTrainedTokenizer   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int] = None   ,						lowerCamelCase   :  List[Any]=False   ,						lowerCamelCase   :  Split = Split.train   ,						)       ->  Union[str, Any]:
								# Load data features from cache or dataset file
								lowerCAmelCase_      :				Optional[int]      =       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_      :				Any      =       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_      :				int      =       torch.load(lowerCamelCase		)
										else:
												logger.info(F'Creating features from dataset file at {data_dir}'		)
												lowerCAmelCase_      :				Optional[Any]      =       token_classification_task.read_examples_from_file(lowerCamelCase   ,						lowerCamelCase		)
												# TODO clean up all this to leverage built-in features of tokenizers
												lowerCAmelCase_      :				str      =       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   :  Any		)       ->  Dict:
								return len(self.features		)





						def __getitem__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]		)       ->  InputFeatures:
								return self.features[i]


if is_tf_available():
		import tensorflow as tf






		class        __snake_case	:







						"""simple docstring"""



						lowercase       			= 42
						lowercase       			= -1_00

						def __init__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  TokenClassificationTask   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  PreTrainedTokenizer   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int] = None   ,						lowerCamelCase   :  str=False   ,						lowerCamelCase   :  Split = Split.train   ,						)       ->  Optional[Any]:
								lowerCAmelCase_      :				Dict      =       token_classification_task.read_examples_from_file(lowerCamelCase   ,						lowerCamelCase		)
								# TODO clean up all this to leverage built-in features of tokenizers
								lowerCAmelCase_      :				str      =       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_      :				Tuple      =       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_      :				str      =       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   :  Tuple		)       ->  str:
								lowerCAmelCase_      :				List[Any]      =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

								return self.dataset

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





						def __getitem__(       self   :  str   ,						lowerCamelCase   :  Union[str, Any]		)       ->  InputFeatures:
								return self.features[i]







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



