



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


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


class       a							(			__lowerCamelCase  ,							__lowerCamelCase				):



		@register_to_config
		def __init__(     self     :str   ,__lowercase     :bool   ,__lowercase     :Optional[int] = None   ,__lowercase     :Optional[int] = None  ):
						super().__init__()

						snake_case__	:      List[Any]     		=	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"

										snake_case__	:      Optional[Any]     		=	torch.zeros(__lowercase   ,__lowercase  )
						else:
										snake_case__	:      Tuple     		=	None

						snake_case__	:      Any     		=	torch.nn.Parameter(__lowercase  )


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    VQModel
		__lowerCAmelCase	:    CLIPTextModel
		__lowerCAmelCase	:    CLIPTokenizer
		__lowerCAmelCase	:    TransformeraDModel
		__lowerCAmelCase	:    LearnedClassifierFreeSamplingEmbeddings
		__lowerCAmelCase	:    VQDiffusionScheduler



		def __init__(     self     :int   ,__lowercase     :VQModel   ,__lowercase     :CLIPTextModel   ,__lowercase     :CLIPTokenizer   ,__lowercase     :TransformeraDModel   ,__lowercase     :VQDiffusionScheduler   ,__lowercase     :LearnedClassifierFreeSamplingEmbeddings   ,):
						super().__init__()

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



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :int   ,__lowercase     :Tuple   ,__lowercase     :Optional[int]  ):
						snake_case__	:      Optional[int]     		=	len(__lowercase  ) if isinstance(__lowercase   ,__lowercase  ) else 1

						# get prompt text embeddings
						snake_case__	:      Tuple     		=	self.tokenizer(
						    __lowercase   ,padding='''max_length'''   ,max_length=self.tokenizer.model_max_length   ,return_tensors='''pt'''   ,)
						snake_case__	:      Any     		=	text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										snake_case__	:      Dict     		=	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}"""  )
										snake_case__	:      Optional[Any]     		=	text_input_ids[:, : self.tokenizer.model_max_length]
						snake_case__	:      Union[str, Any]     		=	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
						snake_case__	:      Optional[Any]     		=	prompt_embeds / prompt_embeds.norm(dim=-1   ,keepdim=__lowercase  )

						# duplicate text embeddings for each generation per prompt
						snake_case__	:      Tuple     		=	prompt_embeds.repeat_interleave(__lowercase   ,dim=0  )

						if do_classifier_free_guidance:
										if self.learned_classifier_free_sampling_embeddings.learnable:
														snake_case__	:      int     		=	self.learned_classifier_free_sampling_embeddings.embeddings
														snake_case__	:      Tuple     		=	negative_prompt_embeds.unsqueeze(0  ).repeat(__lowercase   ,1   ,1  )
										else:
														snake_case__	:      Optional[int]     		=	[''''''] * batch_size

														snake_case__	:      int     		=	text_input_ids.shape[-1]
														snake_case__	:      Optional[int]     		=	self.tokenizer(
														    __lowercase   ,padding='''max_length'''   ,max_length=__lowercase   ,truncation=__lowercase   ,return_tensors='''pt'''   ,)
														snake_case__	:      Union[str, Any]     		=	self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
														# See comment for normalizing text embeddings
														snake_case__	:      Optional[Any]     		=	negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1   ,keepdim=__lowercase  )

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
										snake_case__	:      List[Any]     		=	negative_prompt_embeds.shape[1]
										snake_case__	:      Any     		=	negative_prompt_embeds.repeat(1   ,__lowercase   ,1  )
										snake_case__	:      Dict     		=	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
										snake_case__	:      str     		=	torch.cat([negative_prompt_embeds, prompt_embeds]  )

						return prompt_embeds



		@torch.no_grad()
		def __call__(     self     :Tuple   ,__lowercase     :Union[str, List[str]]   ,__lowercase     :int = 1_0_0   ,__lowercase     :float = 5.0   ,__lowercase     :float = 1.0   ,__lowercase     :int = 1   ,__lowercase     :Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,__lowercase     :Optional[torch.FloatTensor] = None   ,__lowercase     :Optional[str] = "pil"   ,__lowercase     :bool = True   ,__lowercase     :Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,__lowercase     :int = 1   ,):
						if isinstance(__lowercase   ,__lowercase  ):
										snake_case__	:      List[str]     		=	1
						elif isinstance(__lowercase   ,__lowercase  ):
										snake_case__	:      str     		=	len(__lowercase  )
						else:
										raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowercase  )}"""  )

						snake_case__	:      Union[str, Any]     		=	batch_size * num_images_per_prompt

						snake_case__	:      Optional[int]     		=	guidance_scale > 1.0

						snake_case__	:      Any     		=	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

						snake_case__	:      List[str]     		=	(batch_size, self.transformer.num_latent_pixels)
						if latents is None:
										snake_case__	:      List[Any]     		=	self.transformer.num_vector_embeds - 1
										snake_case__	:      Tuple     		=	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)."""  )
										snake_case__	:      Union[str, Any]     		=	latents.to(self.device  )

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

						snake_case__	:      Any     		=	self.scheduler.timesteps.to(self.device  )

						snake_case__	:      int     		=	latents

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

										# predict the un-noised image
										# model_output == `log_p_x_0`
										snake_case__	:      Tuple     		=	self.transformer(__lowercase   ,encoder_hidden_states=__lowercase   ,timestep=__lowercase  ).sample

										if do_classifier_free_guidance:
														snake_case__		, snake_case__	:      Dict     		=	model_output.chunk(2  )
														snake_case__	:      List[Any]     		=	model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
														model_output -= torch.logsumexp(__lowercase   ,dim=1   ,keepdim=__lowercase  )

										snake_case__	:      Optional[int]     		=	self.truncate(__lowercase   ,__lowercase  )

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

										# compute the previous noisy sample x_t -> x_t-1
										snake_case__	:      List[Any]     		=	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  )

						snake_case__	:      Optional[Any]     		=	self.vqvae.config.vq_embed_dim
						snake_case__	:      Optional[int]     		=	(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
						snake_case__	:      Tuple     		=	self.vqvae.quantize.get_codebook_entry(__lowercase   ,shape=__lowercase  )
						snake_case__	:      Any     		=	self.vqvae.decode(__lowercase   ,force_not_quantize=__lowercase  ).sample

						snake_case__	:      Optional[int]     		=	(image / 2 + 0.5).clamp(0   ,1  )
						snake_case__	:      List[Any]     		=	image.cpu().permute(0   ,2   ,3   ,1  ).numpy()

						if output_type == "pil":
										snake_case__	:      Dict     		=	self.numpy_to_pil(__lowercase  )

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=__lowercase  )






		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :torch.FloatTensor   ,__lowercase     :float  ):
						snake_case__		, snake_case__	:      List[str]     		=	torch.sort(__lowercase   ,1   ,descending=__lowercase  )
						snake_case__	:      Dict     		=	torch.exp(__lowercase  )
						snake_case__	:      List[Any]     		=	sorted_p_x_0.cumsum(dim=1  ) < truncation_rate

						# Ensure that at least the largest probability is not zeroed out
						snake_case__	:      Union[str, Any]     		=	torch.full_like(keep_mask[:, 0:1, :]   ,__lowercase  )
						snake_case__	:      Dict     		=	torch.cat((all_true, keep_mask)   ,dim=1  )
						snake_case__	:      str     		=	keep_mask[:, :-1, :]

						snake_case__	:      Any     		=	keep_mask.gather(1   ,indices.argsort(1  )  )

						snake_case__	:      List[str]     		=	log_p_x_0.clone()

						snake_case__	:      List[str]     		=	-torch.inf  # -inf = log(0)

						return rv






A__								=						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__								=						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__								=						{
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				assert len(str(__lowerCAmelCase      )      ) > 2, "year should be in YYYY format"
				assert 1 <= month <= 12, "month should be between 1 to 12"
				assert 1 <= day <= 31, "day should be between 1 to 31"

				# Doomsday algorithm:
				snake_case__	:      Optional[int]     		=	year // 100
				snake_case__	:      List[str]     		=	(5 * (century % 4) + 2) % 7
				snake_case__	:      Dict     		=	year % 100
				snake_case__	:      Union[str, Any]     		=	centurian % 12
				snake_case__	:      List[Any]     		=	(
				    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
				) % 7
				snake_case__	:      List[str]     		=	(
				    DOOMSDAY_NOT_LEAP[month - 1]
				    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
				    else DOOMSDAY_LEAP[month - 1]
				)
				snake_case__	:      List[str]     		=	(dooms_day + day - day_anchor) % 7
				return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

