

"""simple docstring"""






import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCamelCase__				:  int													=  logging.get_logger(__name__)  # pylint: disable=invalid-name
def       UpperCamelCase					(			self     :						Optional[int],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						int )				->			Optional[int]:
	_UpperCAmelCase    :							Optional[int]									=					image.to(device=_lowerCAmelCase )
	_UpperCAmelCase    :							Union[str, Any]									=					image  # take image as image_embeddings
	_UpperCAmelCase    :							Optional[int]									=					image_embeddings.unsqueeze(1 )

	# duplicate image embeddings for each generation per prompt, using mps friendly method
	_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							int									=					image_embeddings.shape
	_UpperCAmelCase    :							Dict									=					image_embeddings.repeat(1,  _lowerCAmelCase,  1 )
	_UpperCAmelCase    :							Dict									=					image_embeddings.view(bs_embed * num_images_per_prompt,  _lowerCAmelCase,  -1 )

	if do_classifier_free_guidance:
		_UpperCAmelCase    :							List[Any]									=					torch.zeros_like(_lowerCAmelCase )

		# 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
		_UpperCAmelCase    :							Dict									=					torch.cat([uncond_embeddings, image_embeddings] )

	return image_embeddings






class     _UpperCAmelCase			(		__a):
			def __init__(		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		)    ->    Union[str, Any]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase    :							Any									=					{"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

				_UpperCAmelCase    :							Any									=					decoder_pipe_kwargs.get("""torch_dtype"""      ,		_A     ) or prior.dtype

				_UpperCAmelCase    :							str									=					StableDiffusionImageVariationPipeline.from_pretrained(
				    """lambdalabs/sd-image-variations-diffusers"""      ,		**_A     )

				# replace `_encode_image` method
				_UpperCAmelCase    :							List[Any]									=					types.MethodType(_encode_image      ,		self.decoder_pipe     )

				self.register_modules(
				    prior=_A      ,		tokenizer=_A      ,		text_encoder=_A      ,		prior_scheduler=_A      ,		)
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		_A = None      ,		)    ->    Union[str, Any]:






				'''simple docstring'''







				if text_model_output is None:
					_UpperCAmelCase    :							str									=					len(_A     ) if isinstance(_A      ,		_A     ) else 1
					# get prompt text embeddings
					_UpperCAmelCase    :							Any									=					self.tokenizer(
					    _A      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		return_tensors="""pt"""      ,		)
					_UpperCAmelCase    :							int									=					text_inputs.input_ids
					_UpperCAmelCase    :							int									=					text_inputs.attention_mask.bool().to(_A     )

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						_UpperCAmelCase    :							Tuple									=					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}'''     )
						_UpperCAmelCase    :							Tuple									=					text_input_ids[:, : self.tokenizer.model_max_length]

					_UpperCAmelCase    :							List[str]									=					self.text_encoder(text_input_ids.to(_A     )     )

					_UpperCAmelCase    :							List[Any]									=					text_encoder_output.text_embeds
					_UpperCAmelCase    :							Dict									=					text_encoder_output.last_hidden_state

				else:
					_UpperCAmelCase    :							Union[str, Any]									=					text_model_output[0].shape[0]
					_UpperCAmelCase						,   _UpperCAmelCase    :							int									=					text_model_output[0], text_model_output[1]
					_UpperCAmelCase    :							Optional[int]									=					text_attention_mask

				_UpperCAmelCase    :							Tuple									=					text_embeddings.repeat_interleave(_A      ,		dim=0     )
				_UpperCAmelCase    :							Tuple									=					text_encoder_hidden_states.repeat_interleave(_A      ,		dim=0     )
				_UpperCAmelCase    :							Tuple									=					text_mask.repeat_interleave(_A      ,		dim=0     )

				if do_classifier_free_guidance:
					_UpperCAmelCase    :							Dict									=					[""""""] * batch_size

					_UpperCAmelCase    :							Dict									=					self.tokenizer(
					    _A      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors="""pt"""      ,		)
					_UpperCAmelCase    :							List[str]									=					uncond_input.attention_mask.bool().to(_A     )
					_UpperCAmelCase    :							Tuple									=					self.text_encoder(uncond_input.input_ids.to(_A     )     )

					_UpperCAmelCase    :							List[Any]									=					uncond_embeddings_text_encoder_output.text_embeds
					_UpperCAmelCase    :							Optional[Any]									=					uncond_embeddings_text_encoder_output.last_hidden_state

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

					_UpperCAmelCase    :							Dict									=					uncond_embeddings.shape[1]
					_UpperCAmelCase    :							Tuple									=					uncond_embeddings.repeat(1      ,		_A     )
					_UpperCAmelCase    :							Any									=					uncond_embeddings.view(batch_size * num_images_per_prompt      ,		_A     )

					_UpperCAmelCase    :							List[str]									=					uncond_text_encoder_hidden_states.shape[1]
					_UpperCAmelCase    :							Optional[Any]									=					uncond_text_encoder_hidden_states.repeat(1      ,		_A      ,		1     )
					_UpperCAmelCase    :							Optional[Any]									=					uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt      ,		_A      ,		-1     )
					_UpperCAmelCase    :							List[Any]									=					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
					_UpperCAmelCase    :							Any									=					torch.cat([uncond_embeddings, text_embeddings]     )
					_UpperCAmelCase    :							Any									=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

					_UpperCAmelCase    :							List[Any]									=					torch.cat([uncond_text_mask, text_mask]     )

				return text_embeddings, text_encoder_hidden_states, text_mask
			@property
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				if self.device != torch.device("""meta"""     ) or not hasattr(self.prior      ,		"""_hf_hook"""     ):
					return self.device
				for module in self.prior.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
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     )    ->    Optional[int]:






				'''simple docstring'''







				if latents is None:
					_UpperCAmelCase    :							Optional[int]									=					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}'''     )
					_UpperCAmelCase    :							Dict									=					latents.to(_A     )

				_UpperCAmelCase    :							List[Any]									=					latents * scheduler.init_noise_sigma
				return latents
			def 							__snake_case (		self      ,		_A = None     )    ->    Tuple:






				'''simple docstring'''







				self.decoder_pipe.to(_A     )
				super().to(_A     )
			@torch.no_grad()
			def __call__(		self      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = 1      ,		_A = 25      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = 4.0      ,		_A = 8.0      ,		_A = 50      ,		_A = 1      ,		_A = 0.0      ,		_A = "pil"      ,		_A = True      ,		)    ->    int:






				'''simple docstring'''







				if prompt is not None:
					if isinstance(_A      ,		_A     ):
						_UpperCAmelCase    :							Tuple									=					1
					elif isinstance(_A      ,		_A     ):
						_UpperCAmelCase    :							Optional[Any]									=					len(_A     )
					else:
						raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_A     )}'''     )
				else:
					_UpperCAmelCase    :							Optional[Any]									=					text_model_output[0].shape[0]

				_UpperCAmelCase    :							Any									=					self._execution_device

				_UpperCAmelCase    :							int									=					batch_size * num_images_per_prompt

				_UpperCAmelCase    :							List[str]									=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

				_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							str									=					self._encode_prompt(
				    _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     )

				# prior

				self.prior_scheduler.set_timesteps(_A      ,		device=_A     )
				_UpperCAmelCase    :							Optional[int]									=					self.prior_scheduler.timesteps

				_UpperCAmelCase    :							Tuple									=					self.prior.config.embedding_dim

				_UpperCAmelCase    :							Dict									=					self.prepare_latents(
				    (batch_size, embedding_dim)      ,		text_embeddings.dtype      ,		_A      ,		_A      ,		_A      ,		self.prior_scheduler      ,		)

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

					_UpperCAmelCase    :							Any									=					self.prior(
					    _A      ,		timestep=_A      ,		proj_embedding=_A      ,		encoder_hidden_states=_A      ,		attention_mask=_A      ,		).predicted_image_embedding

					if do_classifier_free_guidance:
						_UpperCAmelCase						,   _UpperCAmelCase    :							Dict									=					predicted_image_embedding.chunk(2     )
						_UpperCAmelCase    :							Optional[int]									=					predicted_image_embedding_uncond + prior_guidance_scale * (
						    predicted_image_embedding_text - predicted_image_embedding_uncond
						)

					if i + 1 == prior_timesteps_tensor.shape[0]:
						_UpperCAmelCase    :							Optional[Any]									=					None
					else:
						_UpperCAmelCase    :							Dict									=					prior_timesteps_tensor[i + 1]

					_UpperCAmelCase    :							str									=					self.prior_scheduler.step(
					    _A      ,		timestep=_A      ,		sample=_A      ,		generator=_A      ,		prev_timestep=_A      ,		).prev_sample

				_UpperCAmelCase    :							List[Any]									=					self.prior.post_process_latents(_A     )

				_UpperCAmelCase    :							Any									=					prior_latents

				_UpperCAmelCase    :							Any									=					self.decoder_pipe(
				    image=_A      ,		height=_A      ,		width=_A      ,		num_inference_steps=_A      ,		guidance_scale=_A      ,		generator=_A      ,		output_type=_A      ,		return_dict=_A      ,		num_images_per_prompt=_A      ,		eta=_A      ,		)
				return output








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Optional[int]													=  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  ['''T5Tokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[str]													=  ['''T5TokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Optional[Any]													=  [
						    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''T5EncoderModel''',
						    '''T5ForConditionalGeneration''',
						    '''T5Model''',
						    '''T5PreTrainedModel''',
						    '''load_tf_weights_in_t5''',
						    '''T5ForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  int													=  [
						    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFT5EncoderModel''',
						    '''TFT5ForConditionalGeneration''',
						    '''TFT5Model''',
						    '''TFT5PreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''FlaxT5EncoderModel''',
						    '''FlaxT5ForConditionalGeneration''',
						    '''FlaxT5Model''',
						    '''FlaxT5PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

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





