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


def       a			(   self		,	a		,	a		,	a		,	a  )    ->Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   image.to(device=a  )
			SCREAMING_SNAKE_CASE    	=   image  # take image as image_embeddings
			SCREAMING_SNAKE_CASE    	=   image_embeddings.unsqueeze(1  )

			# duplicate image embeddings for each generation per prompt, using mps friendly method
			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   image_embeddings.shape
			SCREAMING_SNAKE_CASE    	=   image_embeddings.repeat(1		,	a		,	1  )
			SCREAMING_SNAKE_CASE    	=   image_embeddings.view(bs_embed * num_images_per_prompt		,	a		,	-1  )

			if do_classifier_free_guidance:
						SCREAMING_SNAKE_CASE    	=   torch.zeros_like(a  )

						# 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([uncond_embeddings, image_embeddings]  )

			return image_embeddings





class 						lowerCamelCase			(					__lowerCamelCase				):







	def __init__(    self			:Optional[int]      ,    lowercase			:PriorTransformer      ,    lowercase			:CLIPTokenizer      ,    lowercase			:CLIPTextModelWithProjection      ,    lowercase			:UnCLIPScheduler      ,    lowercase			:Optional[dict] = None      ,    )		->			List[str]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

				SCREAMING_SNAKE_CASE    	=   decoder_pipe_kwargs.get('''torch_dtype'''      ,    lowercase ) or prior.dtype

				SCREAMING_SNAKE_CASE    	=   StableDiffusionImageVariationPipeline.from_pretrained(
				    '''lambdalabs/sd-image-variations-diffusers'''      ,    **lowercase )

				# replace `_encode_image` method
				SCREAMING_SNAKE_CASE    	=   types.MethodType(_encode_image      ,    self.decoder_pipe )

				self.register_modules(
				    prior=lowercase      ,    tokenizer=lowercase      ,    text_encoder=lowercase      ,    prior_scheduler=lowercase      ,    )







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:str      ,    lowercase			:int      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowercase			:Optional[torch.Tensor] = None      ,    )		->			Dict:







				"""simple docstring"""






				if text_model_output is None:
							SCREAMING_SNAKE_CASE    	=   len(lowercase ) if isinstance(lowercase      ,    lowercase ) else 1
							# get prompt text embeddings
							SCREAMING_SNAKE_CASE    	=   self.tokenizer(
							    lowercase      ,    padding='''max_length'''      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors='''pt'''      ,    )
							SCREAMING_SNAKE_CASE    	=   text_inputs.input_ids
							SCREAMING_SNAKE_CASE    	=   text_inputs.attention_mask.bool().to(lowercase )

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										SCREAMING_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}""" )
										SCREAMING_SNAKE_CASE    	=   text_input_ids[:, : self.tokenizer.model_max_length]

							SCREAMING_SNAKE_CASE    	=   self.text_encoder(text_input_ids.to(lowercase ) )

							SCREAMING_SNAKE_CASE    	=   text_encoder_output.text_embeds
							SCREAMING_SNAKE_CASE    	=   text_encoder_output.last_hidden_state

				else:
							SCREAMING_SNAKE_CASE    	=   text_model_output[0].shape[0]
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   text_model_output[0], text_model_output[1]
							SCREAMING_SNAKE_CASE    	=   text_attention_mask

				SCREAMING_SNAKE_CASE    	=   text_embeddings.repeat_interleave(lowercase      ,    dim=0 )
				SCREAMING_SNAKE_CASE    	=   text_encoder_hidden_states.repeat_interleave(lowercase      ,    dim=0 )
				SCREAMING_SNAKE_CASE    	=   text_mask.repeat_interleave(lowercase      ,    dim=0 )

				if do_classifier_free_guidance:
							SCREAMING_SNAKE_CASE    	=   [''''''] * batch_size

							SCREAMING_SNAKE_CASE    	=   self.tokenizer(
							    lowercase      ,    padding='''max_length'''      ,    max_length=self.tokenizer.model_max_length      ,    truncation=lowercase      ,    return_tensors='''pt'''      ,    )
							SCREAMING_SNAKE_CASE    	=   uncond_input.attention_mask.bool().to(lowercase )
							SCREAMING_SNAKE_CASE    	=   self.text_encoder(uncond_input.input_ids.to(lowercase ) )

							SCREAMING_SNAKE_CASE    	=   uncond_embeddings_text_encoder_output.text_embeds
							SCREAMING_SNAKE_CASE    	=   uncond_embeddings_text_encoder_output.last_hidden_state

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

							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.shape[1]
							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.repeat(1      ,    lowercase )
							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.view(batch_size * num_images_per_prompt      ,    lowercase )

							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.shape[1]
							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.repeat(1      ,    lowercase      ,    1 )
							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.view(
							    batch_size * num_images_per_prompt      ,    lowercase      ,    -1 )
							SCREAMING_SNAKE_CASE    	=   uncond_text_mask.repeat_interleave(lowercase      ,    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([uncond_embeddings, text_embeddings] )
							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 text_embeddings, text_encoder_hidden_states, text_mask







	@property
	def    snake_case__    (    self			:Optional[int] )		->			str:







				"""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(lowercase      ,    '''_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			:List[Any]      ,    lowercase			:Optional[Any]      ,    lowercase			:List[str]      ,    lowercase			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:Union[str, Any] )		->			Optional[Any]:







				"""simple docstring"""






				if latents is None:
							SCREAMING_SNAKE_CASE    	=   randn_tensor(lowercase      ,    generator=lowercase      ,    device=lowercase      ,    dtype=lowercase )
				else:
							if latents.shape != shape:
										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
							SCREAMING_SNAKE_CASE    	=   latents.to(lowercase )

				SCREAMING_SNAKE_CASE    	=   latents * scheduler.init_noise_sigma
				return latents







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Union[str, torch.device]] = None )		->			List[str]:







				"""simple docstring"""






				self.decoder_pipe.to(lowercase )
				super().to(lowercase )







	@torch.no_grad()
	def __call__(    self			:Dict      ,    lowercase			:Optional[Union[str, List[str]]] = None      ,    lowercase			:Optional[int] = None      ,    lowercase			:Optional[int] = None      ,    lowercase			:int = 1      ,    lowercase			:int = 2_5      ,    lowercase			:Optional[torch.Generator] = None      ,    lowercase			:Optional[torch.FloatTensor] = None      ,    lowercase			:Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowercase			:Optional[torch.Tensor] = None      ,    lowercase			:float = 4.0      ,    lowercase			:float = 8.0      ,    lowercase			:int = 5_0      ,    lowercase			:Optional[int] = 1      ,    lowercase			:float = 0.0      ,    lowercase			:Optional[str] = "pil"      ,    lowercase			:bool = True      ,    )		->			List[str]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE    	=   self._execution_device

				SCREAMING_SNAKE_CASE    	=   batch_size * num_images_per_prompt

				SCREAMING_SNAKE_CASE    	=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

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

				# prior

				self.prior_scheduler.set_timesteps(lowercase      ,    device=lowercase )
				SCREAMING_SNAKE_CASE    	=   self.prior_scheduler.timesteps

				SCREAMING_SNAKE_CASE    	=   self.prior.config.embedding_dim

				SCREAMING_SNAKE_CASE    	=   self.prepare_latents(
				    (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    lowercase      ,    lowercase      ,    lowercase      ,    self.prior_scheduler      ,    )

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

							SCREAMING_SNAKE_CASE    	=   self.prior(
							    lowercase      ,    timestep=lowercase      ,    proj_embedding=lowercase      ,    encoder_hidden_states=lowercase      ,    attention_mask=lowercase      ,    ).predicted_image_embedding

							if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   predicted_image_embedding.chunk(2 )
										SCREAMING_SNAKE_CASE    	=   predicted_image_embedding_uncond + prior_guidance_scale * (
										    predicted_image_embedding_text - predicted_image_embedding_uncond
										)

							if i + 1 == prior_timesteps_tensor.shape[0]:
										SCREAMING_SNAKE_CASE    	=   None
							else:
										SCREAMING_SNAKE_CASE    	=   prior_timesteps_tensor[i + 1]

							SCREAMING_SNAKE_CASE    	=   self.prior_scheduler.step(
							    lowercase      ,    timestep=lowercase      ,    sample=lowercase      ,    generator=lowercase      ,    prev_timestep=lowercase      ,    ).prev_sample

				SCREAMING_SNAKE_CASE    	=   self.prior.post_process_latents(lowercase )

				SCREAMING_SNAKE_CASE    	=   prior_latents

				SCREAMING_SNAKE_CASE    	=   self.decoder_pipe(
				    image=lowercase      ,    height=lowercase      ,    width=lowercase      ,    num_inference_steps=lowercase      ,    guidance_scale=lowercase      ,    generator=lowercase      ,    output_type=lowercase      ,    return_dict=lowercase      ,    num_images_per_prompt=lowercase      ,    eta=lowercase      ,    )
				return output

import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand





class 						lowerCamelCase			(					__lowerCamelCase				):







	@staticmethod
	def    snake_case__    (    lowercase			:ArgumentParser )		->			Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''login'''      ,    help='''Log in using the same credentials as on huggingface.co''' )
				login_parser.set_defaults(func=lambda lowercase : LoginCommand(lowercase ) )
				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''whoami'''      ,    help='''Find out which huggingface.co account you are logged in as.''' )
				whoami_parser.set_defaults(func=lambda lowercase : WhoamiCommand(lowercase ) )
				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''logout'''      ,    help='''Log out''' )
				logout_parser.set_defaults(func=lambda lowercase : LogoutCommand(lowercase ) )

				# new system: git-based repo system
				SCREAMING_SNAKE_CASE    	=   parser.add_parser(
				    '''repo'''      ,    help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''      ,    )
				SCREAMING_SNAKE_CASE    	=   repo_parser.add_subparsers(
				    help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands''' )
				SCREAMING_SNAKE_CASE    	=   repo_subparsers.add_parser(
				    '''create'''      ,    help='''Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co''' )
				repo_create_parser.add_argument(
				    '''name'''      ,    type=lowercase      ,    help='''Name for your model\'s repo. Will be namespaced under your username to build the model id.'''      ,    )
				repo_create_parser.add_argument('''--organization'''      ,    type=lowercase      ,    help='''Optional: organization namespace.''' )
				repo_create_parser.add_argument('''-y'''      ,    '''--yes'''      ,    action='''store_true'''      ,    help='''Optional: answer Yes to the prompt''' )
				repo_create_parser.set_defaults(func=lambda lowercase : RepoCreateCommand(lowercase ) )





class 						lowerCamelCase			:
	UpperCamelCase_     :				List[str]								=  '\u001b[1m'
	UpperCamelCase_     :				Union[str, Any]								=  '\u001b[31m'
	UpperCamelCase_     :				Any								=  '\u001b[90m'
	UpperCamelCase_     :				List[str]								=  '\u001b[0m'







	@classmethod
	def    snake_case__    (    cls			:List[Any]      ,    lowercase			:Any )		->			List[str]:







				"""simple docstring"""






				return f"""{cls._bold}{s}{cls._reset}"""







	@classmethod
	def    snake_case__    (    cls			:Optional[Any]      ,    lowercase			:str )		->			Tuple:







				"""simple docstring"""






				return f"""{cls._bold}{cls._red}{s}{cls._reset}"""







	@classmethod
	def    snake_case__    (    cls			:List[str]      ,    lowercase			:Any )		->			List[Any]:







				"""simple docstring"""






				return f"""{cls._gray}{s}{cls._reset}"""



def       a			(   a		,	a  )    ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   [max(len(str(a  )  ) for x in col  ) for col in zip(*a		,	a  )]
			SCREAMING_SNAKE_CASE    	=   ('''{{:{}}} ''' * len(a  )).format(*a  )
			SCREAMING_SNAKE_CASE    	=   []
			lines.append(row_format.format(*a  )  )
			lines.append(row_format.format(*['''-''' * w for w in col_widths]  )  )
			for row in rows:
						lines.append(row_format.format(*a  )  )
			return "\n".join(a  )





class 						lowerCamelCase			:







	def __init__(    self			:List[str]      ,    lowercase			:Optional[Any] )		->			Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   args





class 						lowerCamelCase			(					__lowerCamelCase				):







	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:







				"""simple docstring"""






				print(
				    ANSI.red(
				        '''ERROR! `huggingface-cli login` uses an outdated login mechanism '''
				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
				        '''Please use `huggingface-cli login instead.''' ) )





class 						lowerCamelCase			(					__lowerCamelCase				):







	def    snake_case__    (    self			:List[Any] )		->			Any:







				"""simple docstring"""






				print(
				    ANSI.red(
				        '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
				        '''`huggingface-cli whoami` instead.''' ) )
				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
				if token is None:
							print('''Not logged in''' )
							exit()
				try:
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
							print(lowercase )
							if orgs:
										print(ANSI.bold('''orgs: ''' )      ,    ''','''.join(lowercase ) )
				except HTTPError as e:
							print(lowercase )
							print(ANSI.red(e.response.text ) )
							exit(1 )





class 						lowerCamelCase			(					__lowerCamelCase				):







	def    snake_case__    (    self			:str )		->			Union[str, Any]:







				"""simple docstring"""






				print(
				    ANSI.red(
				        '''ERROR! `transformers-cli logout` uses an outdated logout mechanism '''
				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
				        '''Please use `huggingface-cli logout instead.''' ) )





class 						lowerCamelCase			(					__lowerCamelCase				):







	def    snake_case__    (    self			:int )		->			Optional[Any]:







				"""simple docstring"""






				print(
				    ANSI.red(
				        '''WARNING! Managing repositories through transformers-cli is deprecated. '''
				        '''Please use `huggingface-cli` instead.''' ) )
				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
				if token is None:
							print('''Not logged in''' )
							exit(1 )
				try:
							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git''', '''--version'''] ).decode('''utf-8''' )
							print(ANSI.gray(stdout.strip() ) )
				except FileNotFoundError:
							print('''Looks like you do not have git installed, please install.''' )

				try:
							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git-lfs''', '''--version'''] ).decode('''utf-8''' )
							print(ANSI.gray(stdout.strip() ) )
				except FileNotFoundError:
							print(
							    ANSI.red(
							        '''Looks like you do not have git-lfs installed, please install.'''
							        ''' You can install from https://git-lfs.github.com/.'''
							        ''' Then run `git lfs install` (you only have to do this once).''' ) )
				print('''''' )

				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.args.organization if self.args.organization is not None else user
				SCREAMING_SNAKE_CASE    	=   f"""{namespace}/{self.args.name}"""
				print(f"""You are about to create {ANSI.bold(lowercase )}""" )

				if not self.args.yes:
							SCREAMING_SNAKE_CASE    	=   input('''Proceed? [Y/n] ''' ).lower()
							if not (choice == "" or choice == "y" or choice == "yes"):
										print('''Abort''' )
										exit()
				try:
							SCREAMING_SNAKE_CASE    	=   create_repo(lowercase      ,    name=self.args.name      ,    organization=self.args.organization )
				except HTTPError as e:
							print(lowercase )
							print(ANSI.red(e.response.text ) )
							exit(1 )
				print('''\nYour repo now lives at:''' )
				print(f"""  {ANSI.bold(lowercase )}""" )
				print('''\nYou can clone it locally with the command below, and commit/push as usual.''' )
				print(f"""\n  git clone {url}""" )
				print('''''' )