"""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


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)  # pylint: disable=invalid-name


class 						a__							(       a_				,						a_		):



			@register_to_config
			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None   )			->   str:
								super().__init__()

								__a		 =       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"

													__a		 =       torch.zeros(lowercase_				,						lowercase_   )
								else:
													__a		 =       None

								__a		 =       torch.nn.Parameter(lowercase_   )






class 						a__							(       a_		):
			A__    :       int        =      42
			A__    :       Union[str, Any]        =      42
			A__    :       Dict        =      42
			A__    :       int        =      42
			A__    :       Optional[int]        =      42
			A__    :       Dict        =      42



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)			->   Optional[Any]:
								super().__init__()

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Tuple:
								__a		 =       len(lowercase_   ) if isinstance(lowercase_				,						lowercase_   ) else 1

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

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													__a		 =       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}'''   )
													__a		 =       text_input_ids[:, : self.tokenizer.model_max_length]
								__a		 =       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
								__a		 =       prompt_embeds / prompt_embeds.norm(dim=-1				,						keepdim=lowercase_   )

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

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

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

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

								return prompt_embeds



			@torch.no_grad()
			def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 5.0				,						UpperCAmelCase = 1.0				,						UpperCAmelCase = 1				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = "pil"				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = 1				,						)			->   Tuple:
								if isinstance(lowercase_				,						lowercase_   ):
													__a		 =       1
								elif isinstance(lowercase_				,						lowercase_   ):
													__a		 =       len(lowercase_   )
								else:
													raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}'''   )

								__a		 =       batch_size * num_images_per_prompt

								__a		 =       guidance_scale > 1.0

								__a		 =       self._encode_prompt(lowercase_				,						lowercase_				,						lowercase_   )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(lowercase_				,						lowercase_   ) or callback_steps <= 0)
								):
													raise ValueError(
													    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
													    f''' {type(lowercase_   )}.'''   )

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

								__a		 =       (batch_size, self.transformer.num_latent_pixels)
								if latents is None:
													__a		 =       self.transformer.num_vector_embeds - 1
													__a		 =       torch.full(lowercase_				,						lowercase_   ).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).'''   )
													__a		 =       latents.to(self.device   )

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

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

								__a		 =       latents

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

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

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

													__a		 =       self.truncate(lowercase_				,						lowercase_   )

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

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

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

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

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

								if output_type == "pil":
													__a		 =       self.numpy_to_pil(lowercase_   )

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=lowercase_   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Tuple:
								__a		 =       torch.sort(lowercase_				,						1				,						descending=lowercase_   )
								__a		 =       torch.exp(lowercase_   )
								__a		 =       sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

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

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

								__a		 =       log_p_x_0.clone()

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

								return rv





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




