





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


__lowerCamelCase       :     Dict   =     logging.getLogger(__name__)



@dataclass
class 					A__   :
      _UpperCAmelCase	:str
      _UpperCAmelCase	:List[str]
      _UpperCAmelCase	:Optional[List[str]]



@dataclass
class 					A__   :
      _UpperCAmelCase	:List[int]
      _UpperCAmelCase	:List[int]
      _UpperCAmelCase	:Optional[List[int]]						    =   None
      _UpperCAmelCase	:Optional[List[int]]						    =   None



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   'train'
      _UpperCAmelCase	:Dict						    =   'dev'
      _UpperCAmelCase	:str						    =   'test'



class 					A__   :





      @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'''




       UpperCamelCase	:					Optional[Any]       	=   {label: i for i, label in enumerate(A_   )}

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

        UpperCamelCase	:					Optional[int]       	=   []
        UpperCamelCase	:					Optional[Any]       	=   []
        for word, label in zip(example.words       ,							example.labels   ):
         UpperCamelCase	:					str       	=   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.
        UpperCamelCase	:					Tuple       	=   tokenizer.num_special_tokens_to_add()
        if len(A_   ) > max_seq_length - special_tokens_count:
         UpperCamelCase	:					str       	=   tokens[: (max_seq_length - special_tokens_count)]
         UpperCamelCase	:					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]
        UpperCamelCase	:					Optional[int]       	=   [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:
         UpperCamelCase	:					Optional[Any]       	=   [cls_token] + tokens
         UpperCamelCase	:					Optional[Any]       	=   [pad_token_label_id] + label_ids
         UpperCamelCase	:					Any       	=   [cls_token_segment_id] + segment_ids

        UpperCamelCase	:					Any       	=   tokenizer.convert_tokens_to_ids(A_   )

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

        # Zero-pad up to the sequence length.
        UpperCamelCase	:					Tuple       	=   max_seq_length - len(A_   )
        if pad_on_left:
         UpperCamelCase	:					str       	=   ([pad_token] * padding_length) + input_ids
         UpperCamelCase	:					Dict       	=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
         UpperCamelCase	:					Optional[Any]       	=   ([pad_token_segment_id] * padding_length) + segment_ids
         UpperCamelCase	:					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:
         UpperCamelCase	:					Tuple       	=   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 					A__   (       __snake_case			):
          _UpperCAmelCase	:List[InputFeatures]
          _UpperCAmelCase	:int						    =   nn.CrossEntropyLoss().ignore_index





          def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							A_=False       ,							A_ = Split.train       ,							):
           '''simple docstring'''




           UpperCamelCase	:					Union[str, 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.
           UpperCamelCase	:					List[Any]       	=   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}"""   )
             UpperCamelCase	:					Dict       	=   torch.load(A_   )
            else:
             logger.info(F"""Creating features from dataset file at {data_dir}"""   )
             UpperCamelCase	:					List[Any]       	=   token_classification_task.read_examples_from_file(A_       ,							A_   )
             # TODO clean up all this to leverage built-in features of tokenizers
             UpperCamelCase	:					int       	=   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 					A__   :
          _UpperCAmelCase	:List[InputFeatures]
          _UpperCAmelCase	:int						    =   -1_0_0





          def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							A_=False       ,							A_ = Split.train       ,							):
           '''simple docstring'''




           UpperCamelCase	:					str       	=   token_classification_task.read_examples_from_file(A_       ,							A_   )
           # TODO clean up all this to leverage built-in features of tokenizers
           UpperCamelCase	:					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:
            UpperCamelCase	:					Optional[int]       	=   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:
            UpperCamelCase	:					Optional[int]       	=   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'''




           UpperCamelCase	:					List[str]       	=   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]







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
