code: import json
  import os
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from contextlib import AbstractContextManager
  from typing import Dict, List, Optional
  
  import requests
  
  from ..utils import logging
  from . import BaseTransformersCLICommand
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  lowerCamelCase__           =				'lfs-multipart-upload'
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					@staticmethod
  					def 							UpperCamelCase_						( _lowerCAmelCase :					ArgumentParser						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     parser.add_parser(
  							    "lfs-enable-largefiles"      ,help=(
  							        "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
  							    )      ,)
  							enable_parser.add_argument("path"      ,type=_lowerCAmelCase      ,help="Local path to repository you want to configure."						)
  							enable_parser.set_defaults(func=lambda _lowerCAmelCase						: LfsEnableCommand(_lowerCAmelCase						)						)
  
  							__snake_case		       =     parser.add_parser(
  							    _lowerCAmelCase      ,help=(
  							        "Deprecated: use `huggingface-cli` instead. "
  							        "Command will get called by git-lfs, do not call it directly."
  							    )      ,)
  							upload_parser.set_defaults(func=lambda _lowerCAmelCase						: LfsUploadCommand(_lowerCAmelCase						)						)
  
  
  
  
  class 					UpperCamelCase	:
  
  
  
  
  					def __init__( self :					List[str]      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     args
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							warnings.warn(
  							    "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."						)
  							__snake_case		       =     os.path.abspath(self.args.path						)
  							if not os.path.isdir(_lowerCAmelCase						):
  									print("This does not look like a valid git repo."						)
  									exit(1						)
  							subprocess.run(
  							    "git config lfs.customtransfer.multipart.path transformers-cli".split()      ,check=_lowerCAmelCase      ,cwd=_lowerCAmelCase						)
  							subprocess.run(
  							    F"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()      ,check=_lowerCAmelCase      ,cwd=_lowerCAmelCase      ,)
  							print("Local repo set up for largefiles"						)
  
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   List[str]:
  		__snake_case		       =     json.dumps(__snake_case	) + "\n"
  		sys.stdout.write(__snake_case	)
  		sys.stdout.flush()
  
  
  
  def 							_lowerCamelCase(				)  ->   Optional[Dict]:
  		__snake_case		       =     json.loads(sys.stdin.readline().strip()	)
  
  		if "terminate" in (msg.get("type"	), msg.get("event"	)):
  				# terminate message received
  				return None
  
  		if msg.get("event"	) not in ("download", "upload"):
  				logger.critical("Received unexpected message"	)
  				sys.exit(1	)
  
  		return msg
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     filepath
  							__snake_case		       =     seek_from
  							__snake_case		       =     read_limit
  							__snake_case		       =     0
  
  
  
  
  					def __enter__( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     open(self.filepath      ,"rb"						)
  							self.f.seek(self.seek_from						)
  							return self
  
  
  
  
  					def __len__( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     os.fstat(self.f.fileno()						).st_size
  							return min(self.read_limit      ,total_length - self.seek_from						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					Optional[Any]=-1						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.n_seen >= self.read_limit:
  									return b""
  							__snake_case		       =     self.read_limit - self.n_seen
  							__snake_case		       =     self.f.read(remaining_amount if n < 0 else min(_lowerCAmelCase      ,_lowerCAmelCase						)						)
  							self.n_seen += len(_lowerCAmelCase						)
  							return data
  
  
  
  
  					def __iter__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							yield self.read(n=4 * 1_024 * 1_024						)
  
  
  
  
  
  
  
  					def __exit__( self :					Dict      ,*_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							self.f.close()
  
  
  
  
  class 					UpperCamelCase	:
  
  
  
  
  					def __init__( self :					Dict      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     args
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     json.loads(sys.stdin.readline().strip()						)
  							if not (init_msg.get("event"						) == "init" and init_msg.get("operation"						) == "upload"):
  									write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}						)
  									sys.exit(1						)
  
  							# The transfer process should use the information it needs from the
  							# initiation structure, and also perform any one-off setup tasks it
  							# needs to do. It should then respond on stdout with a simple empty
  							# confirmation structure, as follows:
  							write_msg({}						)
  
  							# After the initiation exchange, git-lfs will send any number of
  							# transfer requests to the stdin of the transfer process, in a serial sequence.
  							while True:
  									__snake_case		       =     read_msg()
  									if msg is None:
  											# When all transfers have been processed, git-lfs will send
  											# a terminate event to the stdin of the transfer process.
  											# On receiving this message the transfer process should
  											# clean up and terminate. No response is expected.
  											sys.exit(0						)
  
  									__snake_case		       =     msg["oid"]
  									__snake_case		       =     msg["path"]
  									__snake_case		       =     msg["action"]["href"]
  									__snake_case		       =     msg["action"]["header"]
  									__snake_case		       =     int(header.pop("chunk_size"						)						)
  									__snake_case		       =     list(header.values()						)
  
  									__snake_case		       =     []
  									for i, presigned_url in enumerate(_lowerCAmelCase						):
  											with FileSlice(_lowerCAmelCase      ,seek_from=i * chunk_size      ,read_limit=_lowerCAmelCase						) as data:
  													__snake_case		       =     requests.put(_lowerCAmelCase      ,data=_lowerCAmelCase						)
  													r.raise_for_status()
  													parts.append(
  													    {
  													        "etag": r.headers.get("etag"						),
  													        "partNumber": i + 1,
  													    }						)
  													# In order to support progress reporting while data is uploading / downloading,
  													# the transfer process should post messages to stdout
  													write_msg(
  													    {
  													        "event": "progress",
  													        "oid": oid,
  													        "bytesSoFar": (i + 1) * chunk_size,
  													        "bytesSinceLast": chunk_size,
  													    }						)
  													# Not precise but that's ok.
  
  									__snake_case		       =     requests.post(
  									    _lowerCAmelCase      ,json={
  									        "oid": oid,
  									        "parts": parts,
  									    }      ,)
  									r.raise_for_status()
  
  									write_msg({"event": "complete", "oid": oid}						)
  
  
  
  
  
  
style_context: import math
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  
  from ...models import PriorTransformer
  from ...pipelines import DiffusionPipeline
  from ...schedulers import HeunDiscreteScheduler
  from ...utils import (
      BaseOutput,
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from .renderer import ShapERenderer
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  lowerCamelCase__           =				'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'
  
  
  
  
  @dataclass
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   42
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					def __init__( self :					int      ,_lowerCAmelCase :					PriorTransformer      ,_lowerCAmelCase :					CLIPTextModelWithProjection      ,_lowerCAmelCase :					CLIPTokenizer      ,_lowerCAmelCase :					HeunDiscreteScheduler      ,_lowerCAmelCase :					ShapERenderer      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__()
  
  							self.register_modules(
  							    prior=_lowerCAmelCase      ,text_encoder=_lowerCAmelCase      ,tokenizer=_lowerCAmelCase      ,scheduler=_lowerCAmelCase      ,renderer=_lowerCAmelCase      ,)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if latents is None:
  									__snake_case		       =     randn_tensor(_lowerCAmelCase      ,generator=_lowerCAmelCase      ,device=_lowerCAmelCase      ,dtype=_lowerCAmelCase						)
  							else:
  									if latents.shape != shape:
  											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""						)
  									__snake_case		       =     latents.to(_lowerCAmelCase						)
  
  							__snake_case		       =     latents * scheduler.init_noise_sigma
  							return latents
  
  
  
  
  					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					Optional[Any]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if is_accelerate_available():
  									from accelerate import cpu_offload
  							else:
  									raise ImportError("Please install accelerate via `pip install accelerate`"						)
  
  							__snake_case		       =     torch.device(F"""cuda:{gpu_id}"""						)
  
  							__snake_case		       =     [self.text_encoder, self.prior]
  							for cpu_offloaded_model in models:
  									if cpu_offloaded_model is not None:
  											cpu_offload(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if is_accelerate_available() and is_accelerate_version(">="      ,"0.17.0.dev0"						):
  									from accelerate import cpu_offload_with_hook
  							else:
  									raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."						)
  
  							__snake_case		       =     torch.device(F"""cuda:{gpu_id}"""						)
  
  							if self.device.type != "cpu":
  									self.to("cpu"      ,silence_dtype_warnings=_lowerCAmelCase						)
  									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  							__snake_case		       =     None
  							for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
  									__snake_case       ,       __snake_case		       =     cpu_offload_with_hook(_lowerCAmelCase      ,_lowerCAmelCase      ,prev_module_hook=_lowerCAmelCase						)
  
  							if self.safety_checker is not None:
  									__snake_case       ,       __snake_case		       =     cpu_offload_with_hook(self.safety_checker      ,_lowerCAmelCase      ,prev_module_hook=_lowerCAmelCase						)
  
  							# We'll offload the last model manually.
  							__snake_case		       =     hook
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.device != torch.device("meta"						) or not hasattr(self.text_encoder      ,"_hf_hook"						):
  									return self.device
  							for module in self.text_encoder.modules():
  									if (
  									    hasattr(_lowerCAmelCase      ,"_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 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					List[Any]      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							len(_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else 1
  
  							# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
  							__snake_case		       =     0
  							# get prompt text embeddings
  							__snake_case		       =     self.tokenizer(
  							    _lowerCAmelCase      ,padding="max_length"      ,max_length=self.tokenizer.model_max_length      ,truncation=_lowerCAmelCase      ,return_tensors="pt"      ,)
  							__snake_case		       =     text_inputs.input_ids
  							__snake_case		       =     self.tokenizer(_lowerCAmelCase      ,padding="longest"      ,return_tensors="pt"						).input_ids
  
  							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_lowerCAmelCase      ,_lowerCAmelCase						):
  									__snake_case		       =     self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]						)
  									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		       =     self.text_encoder(text_input_ids.to(_lowerCAmelCase						)						)
  							__snake_case		       =     text_encoder_output.text_embeds
  
  							__snake_case		       =     prompt_embeds.repeat_interleave(_lowerCAmelCase      ,dim=0						)
  							# in Shap-E it normalize the prompt_embeds and then later rescale it
  							__snake_case		       =     prompt_embeds / torch.linalg.norm(_lowerCAmelCase      ,dim=-1      ,keepdim=_lowerCAmelCase						)
  
  							if do_classifier_free_guidance:
  									__snake_case		       =     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
  									__snake_case		       =     torch.cat([negative_prompt_embeds, prompt_embeds]						)
  
  							# Rescale the features to have unit variance
  							__snake_case		       =     math.sqrt(prompt_embeds.shape[1]						) * prompt_embeds
  
  							return prompt_embeds
  
  
  
  
  
  
  
  					@torch.no_grad()
  					@replace_example_docstring(_lowerCAmelCase						)
  					def __call__( self :					int      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					int = 1      ,_lowerCAmelCase :					int = 25      ,_lowerCAmelCase :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,_lowerCAmelCase :					Optional[torch.FloatTensor] = None      ,_lowerCAmelCase :					float = 4.0      ,_lowerCAmelCase :					int = 64      ,_lowerCAmelCase :					Optional[str] = "pil"      ,_lowerCAmelCase :					bool = True      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
  									__snake_case		       =     1
  							elif isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
  									__snake_case		       =     len(_lowerCAmelCase						)
  							else:
  									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase						)}"""						)
  
  							__snake_case		       =     self._execution_device
  
  							__snake_case		       =     batch_size * num_images_per_prompt
  
  							__snake_case		       =     guidance_scale > 1.0
  							__snake_case		       =     self._encode_prompt(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  
  							# prior
  
  							self.scheduler.set_timesteps(_lowerCAmelCase      ,device=_lowerCAmelCase						)
  							__snake_case		       =     self.scheduler.timesteps
  
  							__snake_case		       =     self.prior.config.num_embeddings
  							__snake_case		       =     self.prior.config.embedding_dim
  
  							__snake_case		       =     self.prepare_latents(
  							    (batch_size, num_embeddings * embedding_dim)      ,prompt_embeds.dtype      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,self.scheduler      ,)
  
  							# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
  							__snake_case		       =     latents.reshape(latents.shape[0]      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  
  							for i, t in enumerate(self.progress_bar(_lowerCAmelCase						)						):
  									# expand the latents if we are doing classifier free guidance
  									__snake_case		       =     torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
  									__snake_case		       =     self.scheduler.scale_model_input(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  									__snake_case		       =     self.prior(
  									    _lowerCAmelCase      ,timestep=_lowerCAmelCase      ,proj_embedding=_lowerCAmelCase      ,).predicted_image_embedding
  
  									# remove the variance
  									__snake_case       ,       __snake_case		       =     noise_pred.split(
  									    scaled_model_input.shape[2]      ,dim=2						)  # batch_size, num_embeddings, embedding_dim
  
  									if do_classifier_free_guidance is not None:
  											__snake_case       ,       __snake_case		       =     noise_pred.chunk(2						)
  											__snake_case		       =     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
  									__snake_case		       =     self.scheduler.step(
  									    _lowerCAmelCase      ,timestep=_lowerCAmelCase      ,sample=_lowerCAmelCase      ,).prev_sample
  
  							if output_type == "latent":
  									return ShapEPipelineOutput(images=_lowerCAmelCase						)
  
  							__snake_case		       =     []
  							for i, latent in enumerate(_lowerCAmelCase						):
  									__snake_case		       =     self.renderer.decode(
  									    latent[None, :]      ,_lowerCAmelCase      ,size=_lowerCAmelCase      ,ray_batch_size=4_096      ,n_coarse_samples=64      ,n_fine_samples=128      ,)
  									images.append(_lowerCAmelCase						)
  
  							__snake_case		       =     torch.stack(_lowerCAmelCase						)
  
  							if output_type not in ["np", "pil"]:
  									raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}"""						)
  
  							__snake_case		       =     images.cpu().numpy()
  
  							if output_type == "pil":
  									__snake_case		       =     [self.numpy_to_pil(_lowerCAmelCase						) for image in images]
  
  							# Offload last model to CPU
  							if hasattr(self      ,"final_offload_hook"						) and self.final_offload_hook is not None:
  									self.final_offload_hook.offload()
  
  							if not return_dict:
  									return (images,)
  
  							return ShapEPipelineOutput(images=_lowerCAmelCase						)
  
  
  
  
  
  