


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


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

_snake_case					:		Union[str, Any]      =    256

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


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

							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  SpectrogramNotesEncoder   ,     lowerCamelCase	:  SpectrogramContEncoder   ,     lowerCamelCase	:  TaFilmDecoder   ,     lowerCamelCase	:  DDPMScheduler   ,     lowerCamelCase	:  OnnxRuntimeModel if is_onnx_available() else Any   ,     )     ->   None:
														super().__init__()

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

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

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  int=(-1.0, 1.0)   ,     lowerCamelCase	:  Any=False    )     ->   List[Any]:
														__snake_case			,      __snake_case		:      str										=       output_range
														if clip:
																					__snake_case		:      Optional[Any]										=       torch.clip(lowerCamelCase   ,     self.min_value   ,     self.max_value    )
														# Scale to [0, 1].
														__snake_case		:      Any										=       (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    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Any=(-1.0, 1.0)   ,     lowerCamelCase	:  Tuple=False    )     ->   Dict:
														__snake_case			,      __snake_case		:      str										=       input_range
														__snake_case		:      Tuple										=       torch.clip(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ) if clip else outputs
														# Scale to [0, 1].
														__snake_case		:      List[str]										=       (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    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  List[Any]    )     ->   List[str]:
														__snake_case		:      Any										=       input_tokens > 0
														__snake_case			,      __snake_case		:      Tuple										=       self.notes_encoder(
														    encoder_input_tokens=lowerCamelCase   ,     encoder_inputs_mask=lowerCamelCase    )

														__snake_case			,      __snake_case		:      Optional[Any]										=       self.continuous_encoder(
														    encoder_inputs=lowerCamelCase   ,     encoder_inputs_mask=lowerCamelCase    )

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

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Optional[Any]    )     ->   Any:
														__snake_case		:      Union[str, Any]										=       noise_time
														if not torch.is_tensor(lowerCamelCase    ):
																					__snake_case		:      Optional[Any]										=       torch.tensor([timesteps]   ,     dtype=torch.long   ,     device=input_tokens.device    )
														elif torch.is_tensor(lowerCamelCase    ) and len(timesteps.shape    ) == 0:
																					__snake_case		:      Union[str, Any]										=       timesteps[None].to(input_tokens.device    )

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

														__snake_case		:      Any										=       self.decoder(
														    encodings_and_masks=lowerCamelCase   ,     decoder_input_tokens=lowerCamelCase   ,     decoder_noise_time=lowerCamelCase    )
														return logits






							@torch.no_grad()
							def __call__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[List[int]]   ,     lowerCamelCase	:  Optional[torch.Generator] = None   ,     lowerCamelCase	:  int = 100   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  str = "numpy"   ,     lowerCamelCase	:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,     lowerCamelCase	:  int = 1   ,     )     ->   Union[AudioPipelineOutput, Tuple]:
														if (callback_steps is None) or (
														    callback_steps is not None and (not isinstance(lowerCamelCase   ,     lowerCamelCase    ) or callback_steps <= 0)
														):
																					raise ValueError(
																					    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																					    F' {type(lowerCamelCase    )}.'    )

														__snake_case		:      Any										=       np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   ,     dtype=np.floataa    )
														__snake_case		:      Union[str, Any]										=       np.zeros([1, 0, self.n_dims]   ,     np.floataa    )
														__snake_case		:      Optional[int]										=       torch.ones((1, TARGET_FEATURE_LENGTH)   ,     dtype=lowerCamelCase   ,     device=self.device    )

														for i, encoder_input_tokens in enumerate(lowerCamelCase    ):
																					if i == 0:
																												__snake_case		:      str										=       torch.from_numpy(pred_mel[:1].copy()    ).to(
																												    device=self.device   ,     dtype=self.decoder.dtype    )
																												# The first chunk has no previous context.
																												__snake_case		:      List[Any]										=       torch.zeros((1, TARGET_FEATURE_LENGTH)   ,     dtype=lowerCamelCase   ,     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.
																												__snake_case		:      List[str]										=       ones

																					__snake_case		:      Tuple										=       self.scale_features(
																					    lowerCamelCase   ,     output_range=[-1.0, 1.0]   ,     clip=lowerCamelCase    )

																					__snake_case		:      int										=       self.encode(
																					    input_tokens=torch.IntTensor([encoder_input_tokens]    ).to(device=self.device    )   ,     continuous_inputs=lowerCamelCase   ,     continuous_mask=lowerCamelCase   ,     )

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

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

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

																												# Compute previous output: x_t -> x_t-1
																												__snake_case		:      str										=       self.scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     generator=lowerCamelCase    ).prev_sample

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

																					__snake_case		:      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(lowerCamelCase   ,     lowerCamelCase    )

																					logger.info("Generated segment"   ,     lowerCamelCase    )

														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":
																					__snake_case		:      Dict										=       self.melgan(input_features=full_pred_mel.astype(np.floataa    )    )
														else:
																					__snake_case		:      List[Any]										=       full_pred_mel

														if not return_dict:
																					return (output,)

														return AudioPipelineOutput(audios=lowerCamelCase    )





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							str					  =  "OwlViTImageProcessor"
							__UpperCAmelCase   :							Dict					  =  ("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(							self	:  str   ,     lowerCamelCase	:  Any=None   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																					    " instead."   ,     lowerCamelCase   ,     )
																					__snake_case		:      List[Any]										=       kwargs.pop("feature_extractor"    )

														__snake_case		:      str										=       image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("You need to specify an `image_processor`."    )
														if tokenizer is None:
																					raise ValueError("You need to specify a `tokenizer`."    )

														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  List[str]="max_length"   ,     lowerCamelCase	:  Dict="np"   ,     **lowerCamelCase	:  str    )     ->   List[Any]:

														if text is None and query_images is None and images is None:
																					raise ValueError(
																					    "You have to specify at least one text or query image or image. All three cannot be none."    )

														if text is not None:
																					if isinstance(lowerCamelCase   ,     lowerCamelCase    ) or (isinstance(lowerCamelCase   ,     lowerCamelCase    ) and not isinstance(text[0]   ,     lowerCamelCase    )):
																												__snake_case		:      Union[str, Any]										=       [self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )]

																					elif isinstance(lowerCamelCase   ,     lowerCamelCase    ) and isinstance(text[0]   ,     lowerCamelCase    ):
																												__snake_case		:      Tuple										=       []

																												# Maximum number of queries across batch
																												__snake_case		:      str										=       max([len(lowerCamelCase    ) for t in text]    )

																												# Pad all batch samples to max number of text queries
																												for t in text:
																																			if len(lowerCamelCase    ) != max_num_queries:
																																										__snake_case		:      Dict										=       t + [" "] * (max_num_queries - len(lowerCamelCase    ))

																																			__snake_case		:      int										=       self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )
																																			encodings.append(lowerCamelCase    )
																					else:
																												raise TypeError("Input text should be a string, a list of strings or a nested list of strings"    )

																					if return_tensors == "np":
																												__snake_case		:      Any										=       np.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Tuple										=       np.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "jax" and is_flax_available():
																												import jax.numpy as jnp

																												__snake_case		:      List[Any]										=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Any										=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "pt" and is_torch_available():
																												import torch

																												__snake_case		:      int										=       torch.cat([encoding["input_ids"] for encoding in encodings]   ,     dim=0    )
																												__snake_case		:      int										=       torch.cat([encoding["attention_mask"] for encoding in encodings]   ,     dim=0    )

																					elif return_tensors == "tf" and is_tf_available():
																												import tensorflow as tf

																												__snake_case		:      int										=       tf.stack([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Dict										=       tf.stack([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					else:
																												raise ValueError("Target return tensor type could not be returned"    )

																					__snake_case		:      Any										=       BatchEncoding()
																					__snake_case		:      Tuple										=       input_ids
																					__snake_case		:      int										=       attention_mask

														if query_images is not None:
																					__snake_case		:      List[Any]										=       BatchEncoding()
																					__snake_case		:      Union[str, Any]										=       self.image_processor(
																					    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    ).pixel_values
																					__snake_case		:      str										=       query_pixel_values

														if images is not None:
																					__snake_case		:      Optional[int]										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )

														if text is not None and images is not None:
																					__snake_case		:      List[str]										=       image_features.pixel_values
																					return encoding
														elif query_images is not None and images is not None:
																					__snake_case		:      int										=       image_features.pixel_values
																					return encoding
														elif text is not None or query_images is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**lowerCamelCase    )   ,     tensor_type=lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   str:
														return self.image_processor.post_process(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  str   ,     **lowerCamelCase	:  List[str]    )     ->   Tuple:
														return self.image_processor.post_process_object_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     *lowerCamelCase	:  Optional[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Any:
														return self.image_processor.post_process_image_guided_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  Optional[int]    )     ->   str:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  List[Any]    )     ->   Tuple:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Any    )     ->   Dict:
														warnings.warn(
														    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor_class






							@property
							def    __snake_case (							self	:  List[str]    )     ->   Union[str, Any]:
														warnings.warn(
														    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor

