




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

a_		:						Optional[Any]             =    '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=8):
			SCREAMING_SNAKE_CASE						= h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			SCREAMING_SNAKE_CASE						= w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor







class 		_snake_case  (  A__					):






			def __init__(       self		,					a		,					a		,					a		,					a		,					a		,					)      ->       int:
						super().__init__()

						self.register_modules(
						    text_encoder=a		,					tokenizer=a		,					unet=a		,					scheduler=a		,					movq=a		,					)
						SCREAMING_SNAKE_CASE						= 2 ** (len(self.movq.config.block_out_channels) - 1)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       str:
						if latents is None:
									SCREAMING_SNAKE_CASE						= randn_tensor(a		,					generator=a		,					device=a		,					dtype=a)
						else:
									if latents.shape != shape:
												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
									SCREAMING_SNAKE_CASE						= latents.to(a)

						SCREAMING_SNAKE_CASE						= latents * scheduler.init_noise_sigma
						return latents






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a=None		,					)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= len(a) if isinstance(a		,					a) else 1
						# get prompt text embeddings
						SCREAMING_SNAKE_CASE						= self.tokenizer(
						    a		,					padding='max_length'		,					truncation=a		,					max_length=77		,					return_attention_mask=a		,					add_special_tokens=a		,					return_tensors='pt'		,					)

						SCREAMING_SNAKE_CASE						= text_inputs.input_ids
						SCREAMING_SNAKE_CASE						= self.tokenizer(a		,					padding='longest'		,					return_tensors='pt').input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a		,					a):
									SCREAMING_SNAKE_CASE						= self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
									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}''')

						SCREAMING_SNAKE_CASE						= text_input_ids.to(a)
						SCREAMING_SNAKE_CASE						= text_inputs.attention_mask.to(a)

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.text_encoder(
						    input_ids=a		,					attention_mask=a)

						SCREAMING_SNAKE_CASE						= prompt_embeds.repeat_interleave(a		,					dim=0)
						SCREAMING_SNAKE_CASE						= text_encoder_hidden_states.repeat_interleave(a		,					dim=0)
						SCREAMING_SNAKE_CASE						= text_mask.repeat_interleave(a		,					dim=0)

						if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE						= 42
									if negative_prompt is None:
												SCREAMING_SNAKE_CASE						= [''] * batch_size
									elif type(a) is not type(a):
												raise TypeError(
												    f'''`negative_prompt` should be the same type to `prompt`, but got {type(a)} !='''
												    f''' {type(a)}.''')
									elif isinstance(a		,					a):
												SCREAMING_SNAKE_CASE						= [negative_prompt]
									elif batch_size != len(a):
												raise ValueError(
												    f'''`negative_prompt`: {negative_prompt} has batch size {len(a)}, but `prompt`:'''
												    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
												    ' the batch size of `prompt`.')
									else:
												SCREAMING_SNAKE_CASE						= negative_prompt

									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    a		,					padding='max_length'		,					max_length=77		,					truncation=a		,					return_attention_mask=a		,					add_special_tokens=a		,					return_tensors='pt'		,					)
									SCREAMING_SNAKE_CASE						= uncond_input.input_ids.to(a)
									SCREAMING_SNAKE_CASE						= uncond_input.attention_mask.to(a)

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.text_encoder(
									    input_ids=a		,					attention_mask=a)

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									SCREAMING_SNAKE_CASE						= negative_prompt_embeds.shape[1]
									SCREAMING_SNAKE_CASE						= negative_prompt_embeds.repeat(1		,					a)
									SCREAMING_SNAKE_CASE						= negative_prompt_embeds.view(batch_size * num_images_per_prompt		,					a)

									SCREAMING_SNAKE_CASE						= uncond_text_encoder_hidden_states.shape[1]
									SCREAMING_SNAKE_CASE						= uncond_text_encoder_hidden_states.repeat(1		,					a		,					1)
									SCREAMING_SNAKE_CASE						= uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt		,					a		,					-1)
									SCREAMING_SNAKE_CASE						= uncond_text_mask.repeat_interleave(a		,					dim=0)

									# done duplicates

									# 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
									SCREAMING_SNAKE_CASE						= torch.cat([negative_prompt_embeds, prompt_embeds])
									SCREAMING_SNAKE_CASE						= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

									SCREAMING_SNAKE_CASE						= torch.cat([uncond_text_mask, text_mask])

						return prompt_embeds, text_encoder_hidden_states, text_mask






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=0)      ->       str:
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError('Please install accelerate via `pip install accelerate`')

						SCREAMING_SNAKE_CASE						= torch.device(f'''cuda:{gpu_id}''')

						SCREAMING_SNAKE_CASE						= [
						    self.unet,
						    self.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(a		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=0)      ->       Dict:
						if is_accelerate_available() and is_accelerate_version('>='		,					'0.17.0.dev0'):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.')

						SCREAMING_SNAKE_CASE						= torch.device(f'''cuda:{gpu_id}''')

						if self.device.type != "cpu":
									self.to('cpu'		,					silence_dtype_warnings=a)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						SCREAMING_SNAKE_CASE						= None
						for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= cpu_offload_with_hook(a		,					a		,					prev_module_hook=a)

						if self.safety_checker is not None:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= cpu_offload_with_hook(self.safety_checker		,					a		,					prev_module_hook=a)

						# We'll offload the last model manually.
						SCREAMING_SNAKE_CASE						= hook






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						if not hasattr(self.unet		,					'_hf_hook'):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(a		,					'_hf_hook')
									    and hasattr(module._hf_hook		,					'execution_device')
									    and module._hf_hook.execution_device is not None
									):
												return torch.device(module._hf_hook.execution_device)
						return self.device







			@torch.no_grad()
			@replace_example_docstring(a)
			def __call__(       self		,					a		,					a		,					a		,					a = None		,					a = 512		,					a = 512		,					a = 100		,					a = 4.0		,					a = 1		,					a = None		,					a = None		,					a = "pil"		,					a = True		,					)      ->       Tuple:

						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= 1
						elif isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= len(a)
						else:
									raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a)}''')

						SCREAMING_SNAKE_CASE						= self._execution_device

						SCREAMING_SNAKE_CASE						= batch_size * num_images_per_prompt
						SCREAMING_SNAKE_CASE						= guidance_scale > 1.0

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._encode_prompt(
						    a		,					a		,					a		,					a		,					a)

						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= torch.cat(a		,					dim=0)
						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= torch.cat(a		,					dim=0)

						if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE						= image_embeds.repeat_interleave(a		,					dim=0)
									SCREAMING_SNAKE_CASE						= negative_image_embeds.repeat_interleave(a		,					dim=0)

						SCREAMING_SNAKE_CASE						= torch.cat([negative_image_embeds, image_embeds]		,					dim=0).to(
						    dtype=prompt_embeds.dtype		,					device=a)

						self.scheduler.set_timesteps(a		,					device=a)
						SCREAMING_SNAKE_CASE						= self.scheduler.timesteps

						SCREAMING_SNAKE_CASE						= self.unet.config.in_channels

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= get_new_h_w(a		,					a		,					self.movq_scale_factor)

						# create initial latent
						SCREAMING_SNAKE_CASE						= self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)		,					text_encoder_hidden_states.dtype		,					a		,					a		,					a		,					self.scheduler		,					)

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

									SCREAMING_SNAKE_CASE						= {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
									SCREAMING_SNAKE_CASE						= self.unet(
									    sample=a		,					timestep=a		,					encoder_hidden_states=a		,					added_cond_kwargs=a		,					return_dict=a		,					)[0]

									if do_classifier_free_guidance:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= noise_pred.split(latents.shape[1]		,					dim=1)
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= noise_pred.chunk(2)
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= variance_pred.chunk(2)
												SCREAMING_SNAKE_CASE						= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												SCREAMING_SNAKE_CASE						= torch.cat([noise_pred, variance_pred_text]		,					dim=1)

									if not (
									    hasattr(self.scheduler.config		,					'variance_type')
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= noise_pred.split(latents.shape[1]		,					dim=1)

									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE						= self.scheduler.step(
									    a		,					a		,					a		,					generator=a		,					).prev_sample
						# post-processing
						SCREAMING_SNAKE_CASE						= self.movq.decode(a		,					force_not_quantize=a)['sample']

						if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''')

						if output_type in ["np", "pil"]:
									SCREAMING_SNAKE_CASE						= image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE						= image.clamp(0		,					1)
									SCREAMING_SNAKE_CASE						= image.cpu().permute(0		,					2		,					3		,					1).float().numpy()

						if output_type == "pil":
									SCREAMING_SNAKE_CASE						= self.numpy_to_pil(a)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=a)












from typing import TYPE_CHECKING

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


a_		:						int             =    {
    '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:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    '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

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




