



"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


class 							lowerCamelCase_(   A__,				A__    ):





					'''simple docstring'''



					@register_to_config
					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None   ):
									super().__init__()

									_lowerCamelCase        =				learnable

									if self.learnable:
													assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
													assert length is not None, "learnable=True requires `length` to be set"

													_lowerCamelCase        =				torch.zeros(lowerCamelCase__				,       lowerCamelCase__   )
									else:
													_lowerCamelCase        =				None

									_lowerCamelCase        =				torch.nn.Parameter(lowerCamelCase__   )


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       VQModel
					lowercase__   :       CLIPTextModel
					lowercase__   :       CLIPTokenizer
					lowercase__   :       TransformeraDModel
					lowercase__   :       LearnedClassifierFreeSamplingEmbeddings
					lowercase__   :       VQDiffusionScheduler

					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									super().__init__()

									self.register_modules(
									    vqvae=lowerCamelCase__				,       transformer=lowerCamelCase__				,       text_encoder=lowerCamelCase__				,       tokenizer=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       learned_classifier_free_sampling_embeddings=lowerCamelCase__				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				len(lowerCamelCase__   ) if isinstance(lowerCamelCase__				,       lowerCamelCase__   ) else 1

									# get prompt text embeddings
									_lowerCamelCase        =				self.tokenizer(
									    lowerCamelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
									_lowerCamelCase        =				text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													_lowerCamelCase        =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
													logger.warning(
													    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
													    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""   )
													_lowerCamelCase        =				text_input_ids[:, : self.tokenizer.model_max_length]
									_lowerCamelCase        =				self.text_encoder(text_input_ids.to(self.device   )   )[0]

									# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
									# While CLIP does normalize the pooled output of the text transformer when combining
									# the image and text embeddings, CLIP does not directly normalize the last hidden state.
									#
									# CLIP normalizing the pooled output.
									# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
									_lowerCamelCase        =				prompt_embeds / prompt_embeds.norm(dim=-1				,       keepdim=lowerCamelCase__   )

									# duplicate text embeddings for each generation per prompt
									_lowerCamelCase        =				prompt_embeds.repeat_interleave(lowerCamelCase__				,       dim=0   )

									if do_classifier_free_guidance:
													if self.learned_classifier_free_sampling_embeddings.learnable:
																	_lowerCamelCase        =				self.learned_classifier_free_sampling_embeddings.embeddings
																	_lowerCamelCase        =				negative_prompt_embeds.unsqueeze(0   ).repeat(lowerCamelCase__				,       1				,       1   )
													else:
																	_lowerCamelCase        =				[''''''] * batch_size

																	_lowerCamelCase        =				text_input_ids.shape[-1]
																	_lowerCamelCase        =				self.tokenizer(
																	    lowerCamelCase__				,       padding='''max_length'''				,       max_length=lowerCamelCase__				,       truncation=lowerCamelCase__				,       return_tensors='''pt'''				,       )
																	_lowerCamelCase        =				self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																	# See comment for normalizing text embeddings
																	_lowerCamelCase        =				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1				,       keepdim=lowerCamelCase__   )

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													_lowerCamelCase        =				negative_prompt_embeds.shape[1]
													_lowerCamelCase        =				negative_prompt_embeds.repeat(1				,       lowerCamelCase__				,       1   )
													_lowerCamelCase        =				negative_prompt_embeds.view(batch_size * num_images_per_prompt				,       lowerCamelCase__				,       -1   )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													_lowerCamelCase        =				torch.cat([negative_prompt_embeds, prompt_embeds]   )

									return prompt_embeds

					@torch.no_grad()
					def __call__(    self				,       lowerCamelCase__				,       lowerCamelCase__ = 1_0_0				,       lowerCamelCase__ = 5.0				,       lowerCamelCase__ = 1.0				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = "pil"				,       lowerCamelCase__ = True				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 1				,       ):
									if isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
													_lowerCamelCase        =				1
									elif isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
													_lowerCamelCase        =				len(lowerCamelCase__   )
									else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__   )}"""   )

									_lowerCamelCase        =				batch_size * num_images_per_prompt

									_lowerCamelCase        =				guidance_scale > 1.0

									_lowerCamelCase        =				self._encode_prompt(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

									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__   )}."""   )

									# get the initial completely masked latents unless the user supplied it

									_lowerCamelCase        =				(batch_size, self.transformer.num_latent_pixels)
									if latents is None:
													_lowerCamelCase        =				self.transformer.num_vector_embeds - 1
													_lowerCamelCase        =				torch.full(lowerCamelCase__				,       lowerCamelCase__   ).to(self.device   )
									else:
													if latents.shape != latents_shape:
																	raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""   )
													if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																	raise ValueError(
																	    '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
																	    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""   )
													_lowerCamelCase        =				latents.to(self.device   )

									# set timesteps
									self.scheduler.set_timesteps(lowerCamelCase__				,       device=self.device   )

									_lowerCamelCase        =				self.scheduler.timesteps.to(self.device   )

									_lowerCamelCase        =				latents

									for i, t in enumerate(self.progress_bar(lowerCamelCase__   )   ):
													# expand the sample if we are doing classifier free guidance
													_lowerCamelCase        =				torch.cat([sample] * 2   ) if do_classifier_free_guidance else sample

													# predict the un-noised image
													# model_output == `log_p_x_0`
													_lowerCamelCase        =				self.transformer(lowerCamelCase__				,       encoder_hidden_states=lowerCamelCase__				,       timestep=lowerCamelCase__   ).sample

													if do_classifier_free_guidance:
																	_lowerCamelCase  ,     _lowerCamelCase        =				model_output.chunk(2   )
																	_lowerCamelCase        =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(lowerCamelCase__				,       dim=1				,       keepdim=lowerCamelCase__   )

													_lowerCamelCase        =				self.truncate(lowerCamelCase__				,       lowerCamelCase__   )

													# remove `log(0)`'s (`-inf`s)
													_lowerCamelCase        =				model_output.clamp(-7_0   )

													# compute the previous noisy sample x_t -> x_t-1
													_lowerCamelCase        =				self.scheduler.step(lowerCamelCase__				,       timestep=lowerCamelCase__				,       sample=lowerCamelCase__				,       generator=lowerCamelCase__   ).prev_sample

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

									_lowerCamelCase        =				self.vqvae.config.vq_embed_dim
									_lowerCamelCase        =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
									_lowerCamelCase        =				self.vqvae.quantize.get_codebook_entry(lowerCamelCase__				,       shape=lowerCamelCase__   )
									_lowerCamelCase        =				self.vqvae.decode(lowerCamelCase__				,       force_not_quantize=lowerCamelCase__   ).sample

									_lowerCamelCase        =				(image / 2 + 0.5).clamp(0				,       1   )
									_lowerCamelCase        =				image.cpu().permute(0				,       2				,       3				,       1   ).numpy()

									if output_type == "pil":
													_lowerCamelCase        =				self.numpy_to_pil(lowerCamelCase__   )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				torch.sort(lowerCamelCase__				,       1				,       descending=lowerCamelCase__   )
									_lowerCamelCase        =				torch.exp(lowerCamelCase__   )
									_lowerCamelCase        =				sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

									# Ensure that at least the largest probability is not zeroed out
									_lowerCamelCase        =				torch.full_like(keep_mask[:, 0:1, :]				,       lowerCamelCase__   )
									_lowerCamelCase        =				torch.cat((all_true, keep_mask)				,       dim=1   )
									_lowerCamelCase        =				keep_mask[:, :-1, :]

									_lowerCamelCase        =				keep_mask.gather(1				,       indices.argsort(1   )   )

									_lowerCamelCase        =				log_p_x_0.clone()

									_lowerCamelCase        =				-torch.inf  # -inf = log(0)

									return rv





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
