

'''simple docstring'''





import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
	        f"""stage{idx}.patch_embed.proj.weight""",
	    )	)
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
	        f"""stage{idx}.patch_embed.proj.bias""",
	    )	)
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
	        f"""stage{idx}.patch_embed.norm.weight""",
	    )	)
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
	        f"""stage{idx}.patch_embed.norm.bias""",
	    )	)
	return embed





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
	    )	)
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
	    )	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")	)
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")	)
	return attention_weights





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')	)
	return token





def a():





	'''simple docstring'''
	snake_case_      		= []
	head.append(('layernorm.weight', 'norm.weight')	)
	head.append(('layernorm.bias', 'norm.bias')	)
	head.append(('classifier.weight', 'head.weight')	)
	head.append(('classifier.bias', 'head.bias')	)
	return head





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 'imagenet-1k-id2label.json'
	snake_case_      		= 1000

	snake_case_      		= 'huggingface/label-files'
	snake_case_      		= num_labels
	snake_case_      		= json.load(open(cached_download(hf_hub_url(lowercase__			, lowercase__			, repo_type='dataset'	)	)			, 'r'	)	)
	snake_case_      		= {int(lowercase__	): v for k, v in idalabel.items()}

	snake_case_      		= idalabel
	snake_case_      		= {v: k for k, v in idalabel.items()}

	snake_case_      		= snake_case_      		= CvtConfig(num_labels=lowercase__			, idalabel=lowercase__			, labelaid=lowercase__	)

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit('/'			, 1	)[-1][4:6] == "13":
		snake_case_      		= [1, 2, 10]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit('/'			, 1	)[-1][4:6] == "21":
		snake_case_      		= [1, 4, 16]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		snake_case_      		= [2, 2, 20]
		snake_case_      		= [3, 12, 16]
		snake_case_      		= [192, 768, 1024]

	snake_case_      		= CvtForImageClassification(lowercase__	)
	snake_case_      		= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'	)
	snake_case_      		= image_size
	snake_case_      		= torch.load(lowercase__			, map_location=torch.device('cpu'	)	)

	snake_case_      		= OrderedDict()
	snake_case_      		= []

	for idx in range(len(config.depth	)	):
		if config.cls_token[idx]:
			snake_case_      		= list_of_state_dict + cls_token(lowercase__	)
		snake_case_      		= list_of_state_dict + embeddings(lowercase__	)
		for cnt in range(config.depth[idx]	):
			snake_case_      		= list_of_state_dict + attention(lowercase__			, lowercase__	)

	snake_case_      		= list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(lowercase__	)
	for i in range(len(lowercase__	)	):
		snake_case_      		= original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(lowercase__	)
	model.save_pretrained(lowercase__	)
	image_processor.save_pretrained(lowercase__	)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		parser.add_argument(
		    '--cvt_model',
		    default='cvt-w24',
		    type=str,
		    help='Name of the cvt model you\'d like to convert.',
		)
		parser.add_argument(
		    '--image_size',
		    default=384,
		    type=int,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--cvt_file_name',
		    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
		    type=str,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		A											=     parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



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 SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= 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_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

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

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_      		= 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_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				snake_case_      		= 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_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

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

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

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

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

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


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

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				snake_case_      		= self._encode_prompt(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

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

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

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					snake_case_      		= torch.full(__UpperCamelCase ,				__UpperCamelCase							).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_      		= latents.to(self.device							)

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

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

				snake_case_      		= latents

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

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

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

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

					# remove `log(0)`'s (`-inf`s)
					snake_case_      		= model_output.clamp(-70							)

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

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

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

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

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

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

				snake_case_      		= log_p_x_0.clone()

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

				return rv
