





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__=13,		__magic_name__=7,		__magic_name__=True,		__magic_name__=True,		__magic_name__=True,		__magic_name__=True,		__magic_name__=99,		__magic_name__=32,		__magic_name__=5,		__magic_name__=4,		__magic_name__=37,		__magic_name__="gelu",		__magic_name__=0.1,		__magic_name__=0.1,		__magic_name__=512,		__magic_name__=16,		__magic_name__=2,		__magic_name__=0.02,		__magic_name__=False,		__magic_name__=True,		__magic_name__="None",		__magic_name__=3,		__magic_name__=4,		__magic_name__=None,		) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      parent
       UpperCamelCase__							:				List[Any]          =      batch_size
       UpperCamelCase__							:				Tuple          =      seq_length
       UpperCamelCase__							:				Optional[int]          =      is_training
       UpperCamelCase__							:				Any          =      use_input_mask
       UpperCamelCase__							:				Optional[Any]          =      use_token_type_ids
       UpperCamelCase__							:				Tuple          =      use_labels
       UpperCamelCase__							:				Union[str, Any]          =      vocab_size
       UpperCamelCase__							:				List[Any]          =      hidden_size
       UpperCamelCase__							:				Dict          =      num_hidden_layers
       UpperCamelCase__							:				List[str]          =      num_attention_heads
       UpperCamelCase__							:				str          =      intermediate_size
       UpperCamelCase__							:				int          =      hidden_act
       UpperCamelCase__							:				int          =      hidden_dropout_prob
       UpperCamelCase__							:				List[Any]          =      attention_probs_dropout_prob
       UpperCamelCase__							:				Optional[Any]          =      max_position_embeddings
       UpperCamelCase__							:				List[str]          =      type_vocab_size
       UpperCamelCase__							:				Optional[int]          =      type_sequence_label_size
       UpperCamelCase__							:				Optional[int]          =      initializer_range
       UpperCamelCase__							:				Optional[Any]          =      num_labels
       UpperCamelCase__							:				str          =      num_choices
       UpperCamelCase__							:				str          =      relative_attention
       UpperCamelCase__							:				Optional[int]          =      position_biased_input
       UpperCamelCase__							:				int          =      pos_att_type
       UpperCamelCase__							:				Union[str, Any]          =      scope




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      ids_tensor([self.batch_size, self.seq_length],		self.vocab_size							)

       UpperCamelCase__							:				str          =      None
       if self.use_input_mask:
           UpperCamelCase__							:				Optional[int]          =      ids_tensor([self.batch_size, self.seq_length],		vocab_size=2							)

       UpperCamelCase__							:				Optional[int]          =      None
       if self.use_token_type_ids:
           UpperCamelCase__							:				int          =      ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size							)

       UpperCamelCase__							:				str          =      None
       UpperCamelCase__							:				str          =      None
       UpperCamelCase__							:				Optional[Any]          =      None
       if self.use_labels:
           UpperCamelCase__							:				Any          =      ids_tensor([self.batch_size],		self.type_sequence_label_size							)
           UpperCamelCase__							:				Optional[int]          =      ids_tensor([self.batch_size, self.seq_length],		self.num_labels							)
           UpperCamelCase__							:				int          =      ids_tensor([self.batch_size],		self.num_choices							)

       UpperCamelCase__							:				Any          =      self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       return DebertaConfig(
           vocab_size=self.vocab_size,		hidden_size=self.hidden_size,		num_hidden_layers=self.num_hidden_layers,		num_attention_heads=self.num_attention_heads,		intermediate_size=self.intermediate_size,		hidden_act=self.hidden_act,		hidden_dropout_prob=self.hidden_dropout_prob,		attention_probs_dropout_prob=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		type_vocab_size=self.type_vocab_size,		initializer_range=self.initializer_range,		relative_attention=self.relative_attention,		position_biased_input=self.position_biased_input,		pos_att_type=self.pos_att_type,		)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.get_config()
       UpperCamelCase__							:				Any          =      300
       return config




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       self.parent.assertListEqual(list(result.loss.size()							),		[]							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      DebertaModel(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				str          =      model(__magic_name__,		attention_mask=__magic_name__,		token_type_ids=__magic_name__							)[0]
       UpperCamelCase__							:				Tuple          =      model(__magic_name__,		token_type_ids=__magic_name__							)[0]
       UpperCamelCase__							:				Dict          =      model(__magic_name__							)[0]

       self.parent.assertListEqual(list(sequence_output.size()							),		[self.batch_size, self.seq_length, self.hidden_size]							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      DebertaForMaskedLM(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Any          =      model(__magic_name__,		attention_mask=__magic_name__,		token_type_ids=__magic_name__,		labels=__magic_name__							)

       self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.seq_length, self.vocab_size)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.num_labels
       UpperCamelCase__							:				Optional[Any]          =      DebertaForSequenceClassification(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				int          =      model(__magic_name__,		attention_mask=__magic_name__,		token_type_ids=__magic_name__,		labels=__magic_name__							)
       self.parent.assertListEqual(list(result.logits.size()							),		[self.batch_size, self.num_labels]							)
       self.check_loss_output(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.num_labels
       UpperCamelCase__							:				Optional[int]          =      DebertaForTokenClassification(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Union[str, Any]          =      model(__magic_name__,		attention_mask=__magic_name__,		token_type_ids=__magic_name__,		labels=__magic_name__							)
       self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.seq_length, self.num_labels)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      DebertaForQuestionAnswering(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Tuple          =      model(
           __magic_name__,		attention_mask=__magic_name__,		token_type_ids=__magic_name__,		start_positions=__magic_name__,		end_positions=__magic_name__,		)
       self.parent.assertEqual(result.start_logits.shape,		(self.batch_size, self.seq_length)							)
       self.parent.assertEqual(result.end_logits.shape,		(self.batch_size, self.seq_length)							)




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,
       )							:				str          =      config_and_inputs
       UpperCamelCase__							:				Optional[Any]          =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							List[Any]   		=		(
       (
           DebertaModel,
           DebertaForMaskedLM,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   a						:							Any   		=		(
       {
           "feature-extraction": DebertaModel,
           "fill-mask": DebertaForMaskedLM,
           "question-answering": DebertaForQuestionAnswering,
           "text-classification": DebertaForSequenceClassification,
           "token-classification": DebertaForTokenClassification,
           "zero-shot": DebertaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   a						:							Tuple   		=		True
   a						:							Union[str, Any]   		=		False
   a						:							List[str]   		=		False
   a						:							Dict   		=		False
   a						:							Optional[int]   		=		False




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      DebertaModelTester(self							)
       UpperCamelCase__							:				str          =      ConfigTester(self,		config_class=__magic_name__,		hidden_size=37							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       self.config_tester.run_common_tests()




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*__magic_name__							)




   @slow
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:				Optional[Any]          =      DebertaModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)






@require_torch
@require_sentencepiece
@require_tokenizers
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   @unittest.skip(reason='''Model not available yet'''							)
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       pass




   @slow
   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      DebertaModel.from_pretrained('''microsoft/deberta-base'''							)

       UpperCamelCase__							:				Tuple          =      torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
       UpperCamelCase__							:				Any          =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
       with torch.no_grad():
           UpperCamelCase__							:				Any          =      model(__magic_name__,		attention_mask=__magic_name__							)[0]
       # compare the actual values for a slice.
       UpperCamelCase__							:				Optional[Any]          =      torch.tensor(
           [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]							)
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4],		__magic_name__,		atol=1E-4							),		f"{output[:, 1:4, 1:4]}"							)








import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


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

UpperCAmelCase_     							= 256






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		["melgan"]




   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      None:






       """simple docstring"""
       super().__init__()

       # From MELGAN
       UpperCamelCase__							:				Optional[int]          =      math.log(1E-5							)  # Matches MelGAN training.
       UpperCamelCase__							:				int          =      4.0  # Largest value for most examples
       UpperCamelCase__							:				Optional[int]          =      128

       self.register_modules(
           notes_encoder=__magic_name__,		continuous_encoder=__magic_name__,		decoder=__magic_name__,		scheduler=__magic_name__,		melgan=__magic_name__,		)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Any:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      output_range
       if clip:
           UpperCamelCase__							:				Union[str, Any]          =      torch.clip(__magic_name__,		self.min_value,		self.max_value							)
       # Scale to [0, 1].
       UpperCamelCase__							:				List[str]          =      (features - self.min_value) / (self.max_value - self.min_value)
       # Scale to [min_out, max_out].
       return zero_one * (max_out - min_out) + min_out




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      input_range
       UpperCamelCase__							:				Any          =      torch.clip(__magic_name__,		__magic_name__,		__magic_name__							) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase__							:				Any          =      (outputs - min_out) / (max_out - min_out)
       # Scale to [self.min_value, self.max_value].
       return zero_one * (self.max_value - self.min_value) + self.min_value




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      input_tokens > 0
       UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.notes_encoder(
           encoder_input_tokens=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.continuous_encoder(
           encoder_inputs=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      noise_time
       if not torch.is_tensor(__magic_name__							):
           UpperCamelCase__							:				Tuple          =      torch.tensor([timesteps],		dtype=torch.long,		device=input_tokens.device							)
       elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
           UpperCamelCase__							:				Union[str, Any]          =      timesteps[None].to(input_tokens.device							)

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       UpperCamelCase__							:				Dict          =      timesteps * torch.ones(input_tokens.shape[0],		dtype=timesteps.dtype,		device=timesteps.device							)

       UpperCamelCase__							:				List[str]          =      self.decoder(
           encodings_and_masks=__magic_name__,		decoder_input_tokens=__magic_name__,		decoder_noise_time=__magic_name__							)
       return logits




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = 100,		__magic_name__ = True,		__magic_name__ = "numpy",		__magic_name__ = None,		__magic_name__ = 1,		) ->      Union[AudioPipelineOutput, Tuple]:






       """simple docstring"""
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__magic_name__,		__magic_name__							) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__magic_name__							)}."							)

       UpperCamelCase__							:				Dict          =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],		dtype=np.floataa							)
       UpperCamelCase__							:				Tuple          =      np.zeros([1, 0, self.n_dims],		np.floataa							)
       UpperCamelCase__							:				List[Any]          =      torch.ones((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)

       for i, encoder_input_tokens in enumerate(__magic_name__							):
           if i == 0:
               UpperCamelCase__							:				str          =      torch.from_numpy(pred_mel[:1].copy()							).to(
                   device=self.device,		dtype=self.decoder.dtype							)
               # The first chunk has no previous context.
               UpperCamelCase__							:				Any          =      torch.zeros((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)
           else:
               # The full song pipeline does not feed in a context feature, so the mask
               # will be all 0s after the feature converter. Because we know we're
               # feeding in a full context chunk from the previous prediction, set it
               # to all 1s.
               UpperCamelCase__							:				List[str]          =      ones

           UpperCamelCase__							:				int          =      self.scale_features(
               __magic_name__,		output_range=[-1.0, 1.0],		clip=__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							),		continuous_inputs=__magic_name__,		continuous_mask=__magic_name__,		)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           UpperCamelCase__							:				Optional[int]          =      randn_tensor(
               shape=encoder_continuous_inputs.shape,		generator=__magic_name__,		device=self.device,		dtype=self.decoder.dtype,		)

           # set step values
           self.scheduler.set_timesteps(__magic_name__							)

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCamelCase__							:				Union[str, Any]          =      self.decode(
                   encodings_and_masks=__magic_name__,		input_tokens=__magic_name__,		noise_time=t / self.scheduler.config.num_train_timesteps,		)

               # Compute previous output: x_t -> x_t-1
               UpperCamelCase__							:				List[Any]          =      self.scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				List[Any]          =      self.scale_to_features(__magic_name__,		input_range=[-1.0, 1.0]							)
           UpperCamelCase__							:				List[Any]          =      mel[:1]
           UpperCamelCase__							:				int          =      mel.cpu().float().numpy()

           UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([full_pred_mel, pred_mel[:1]],		axis=1							)

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
               callback(__magic_name__,		__magic_name__							)

           logger.info('''Generated segment''',		__magic_name__							)

       if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''							)
       elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''							)

       if output_type == "numpy":
           UpperCamelCase__							:				Optional[int]          =      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
           UpperCamelCase__							:				Any          =      full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=__magic_name__							)

