


"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class 					lowerCAmelCase_      (     _lowerCamelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  ["""vqvae"""]

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__()
										self.register_modules(unet=lowercase_						,      scheduler=lowercase_						,      mel=lowercase_						,      vqvae=lowercase_      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 50 if isinstance(self.scheduler						,      lowercase_      ) else 10_00

				@torch.no_grad()
				def __call__(			self						,      lowerCAmelCase = 1						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = 0						,      lowerCAmelCase = 0						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = 0						,      lowerCAmelCase = 0						,      lowerCAmelCase = None						,      lowerCAmelCase = 0						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase=True						,      ):






										"""simple docstring"""

										snake_case        =     steps or self.get_default_steps()
										self.scheduler.set_timesteps(lowercase_      )
										snake_case        =     step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size      ) == int:
																snake_case        =     (self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
																snake_case        =     randn_tensor(
																    (
																        batch_size,
																        self.unet.config.in_channels,
																        self.unet.config.sample_size[0],
																        self.unet.config.sample_size[1],
																    )						,      generator=lowercase_						,      device=self.device						,      )
										snake_case        =     noise
										snake_case        =     None

										if audio_file is not None or raw_audio is not None:
																self.mel.load_audio(lowercase_						,      lowercase_      )
																snake_case        =     self.mel.audio_slice_to_image(lowercase_      )
																snake_case        =     np.frombuffer(input_image.tobytes()						,      dtype='uint8'      ).reshape(
																    (input_image.height, input_image.width)      )
																snake_case        =     (input_image / 2_55) * 2 - 1
																snake_case        =     torch.tensor(input_image[np.newaxis, :, :]						,      dtype=torch.float      ).to(self.device      )

																if self.vqvae is not None:
																						snake_case        =     self.vqvae.encode(torch.unsqueeze(lowercase_						,      0      )      ).latent_dist.sample(
																						    generator=lowercase_      )[0]
																						snake_case        =     self.vqvae.config.scaling_factor * input_images

																if start_step > 0:
																						snake_case        =     self.scheduler.add_noise(lowercase_						,      lowercase_						,      self.scheduler.timesteps[start_step - 1]      )

																snake_case        =     (
																    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																)
																snake_case        =     int(mask_start_secs * pixels_per_second      )
																snake_case        =     int(mask_end_secs * pixels_per_second      )
																snake_case        =     self.scheduler.add_noise(lowercase_						,      lowercase_						,      torch.tensor(self.scheduler.timesteps[start_step:]      )      )

										for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]      )      ):
																if isinstance(self.unet						,      lowercase_      ):
																						snake_case        =     self.unet(lowercase_						,      lowercase_						,      lowercase_      )['sample']
																else:
																						snake_case        =     self.unet(lowercase_						,      lowercase_      )['sample']

																if isinstance(self.scheduler						,      lowercase_      ):
																						snake_case        =     self.scheduler.step(
																						    model_output=lowercase_						,      timestep=lowercase_						,      sample=lowercase_						,      eta=lowercase_						,      generator=lowercase_						,      )['prev_sample']
																else:
																						snake_case        =     self.scheduler.step(
																						    model_output=lowercase_						,      timestep=lowercase_						,      sample=lowercase_						,      generator=lowercase_						,      )['prev_sample']

																if mask is not None:
																						if mask_start > 0:
																												snake_case        =     mask[:, step, :, :mask_start]
																						if mask_end > 0:
																												snake_case        =     mask[:, step, :, -mask_end:]

										if self.vqvae is not None:
																# 0.18215 was scaling factor used in training to ensure unit variance
																snake_case        =     1 / self.vqvae.config.scaling_factor * images
																snake_case        =     self.vqvae.decode(lowercase_      )['sample']

										snake_case        =     (images / 2 + 0.5).clamp(0						,      1      )
										snake_case        =     images.cpu().permute(0						,      2						,      3						,      1      ).numpy()
										snake_case        =     (images * 2_55).round().astype('uint8'      )
										snake_case        =     list(
										    (Image.fromarray(_[:, :, 0]      ) for _ in images)
										    if images.shape[3] == 1
										    else (Image.fromarray(lowercase_						,      mode='RGB'      ).convert('L'      ) for _ in images)      )

										snake_case        =     [self.mel.image_to_audio(lowercase_      ) for _ in images]
										if not return_dict:
																return images, (self.mel.get_sample_rate(), audios)

										return BaseOutput(**AudioPipelineOutput(np.array(lowercase_      )[:, np.newaxis, :]      )						,      **ImagePipelineOutput(lowercase_      )      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = 50      ):






										"""simple docstring"""

										assert isinstance(self.scheduler						,      lowercase_      )
										self.scheduler.set_timesteps(lowercase_      )
										snake_case        =     np.array(
										    [np.frombuffer(image.tobytes()						,      dtype='uint8'      ).reshape((1, image.height, image.width)      ) for image in images]      )
										snake_case        =     (sample / 2_55) * 2 - 1
										snake_case        =     torch.Tensor(lowercase_      ).to(self.device      )

										for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,      (0,)      )      ):
																snake_case        =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																snake_case        =     self.scheduler.alphas_cumprod[t]
																snake_case        =     (
																    self.scheduler.alphas_cumprod[prev_timestep]
																    if prev_timestep >= 0
																    else self.scheduler.final_alpha_cumprod
																)
																snake_case        =     1 - alpha_prod_t
																snake_case        =     self.unet(lowercase_						,      lowercase_      )['sample']
																snake_case        =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
																snake_case        =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																snake_case        =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

										return sample

				@staticmethod
				def 						snake_case	(			lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     acos(torch.dot(torch.flatten(lowercase_      )						,      torch.flatten(lowercase_      )      ) / torch.norm(lowercase_      ) / torch.norm(lowercase_      )      )
										return sin((1 - alpha) * theta      ) * xa / sin(lowercase_      ) + sin(alpha * theta      ) * xa / sin(lowercase_      )



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=2						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase="None"						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     relative_attention
										snake_case        =     position_biased_input
										snake_case        =     pos_att_type
										snake_case        =     scope

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     random_attention_mask([self.batch_size, self.seq_length]      )

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

										snake_case        =     None
										snake_case        =     None
										snake_case        =     None
										if self.use_labels:
																snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )

										snake_case        =     DebertaVaConfig(
										    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						,      relative_attention=self.relative_attention						,      position_biased_input=self.position_biased_input						,      initializer_range=self.initializer_range						,      return_dict=lowerCAmelCase						,      )

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel(config=lowerCAmelCase      )
										snake_case        =     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

										snake_case        =     [input_ids, input_mask]
										snake_case        =     model(lowerCAmelCase      )

										snake_case        =     model(lowerCAmelCase      )

										self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaForMaskedLM(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFDebertaVaForSequenceClassification(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFDebertaVaForTokenClassification(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaForQuestionAnswering(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case        =     model(lowerCAmelCase      )
										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 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     config_and_inputs
										snake_case        =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_tf
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[int]     					=  (
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase       :      Any     					=  (
				    {
				        """feature-extraction""": TFDebertaVaModel,
				        """fill-mask""": TFDebertaVaForMaskedLM,
				        """question-answering""": TFDebertaVaForQuestionAnswering,
				        """text-classification""": TFDebertaVaForSequenceClassification,
				        """token-classification""": TFDebertaVaForTokenClassification,
				        """zero-shot""": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase       :      Optional[Any]     					=  False
				_lowerCAmelCase       :      List[Any]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
										self.assertIsNotNone(lowerCAmelCase      )





@require_tf
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@unittest.skip(reason='Model not available yet'      )
				def 						snake_case	(			self      ):






										"""simple docstring"""

										pass

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
										snake_case        =     tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
										snake_case        =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )[0]

										snake_case        =     tf.constant(
										    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]      )
										tf.debugging.assert_near(output[:, 1:4, 1:4]						,      lowerCAmelCase						,      atol=1E-4      )


