code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  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
  
  
  __A :		Dict      							=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __A :		int      							=    '''lfs-multipart-upload'''
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     @staticmethod
     def       a_							(							SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							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=SCREAMING_SNAKE_CASE_    ,      help="""Local path to repository you want to configure.""" )
         enable_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE_ : LfsEnableCommand(SCREAMING_SNAKE_CASE_ ) )
  
         UpperCamelCase				:   int          =							parser.add_parser(
             SCREAMING_SNAKE_CASE_    ,      help=(
                 """Deprecated: use `huggingface-cli` instead. """
                 """Command will get called by git-lfs, do not call it directly."""
             )    ,      )
         upload_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE_ : LfsUploadCommand(SCREAMING_SNAKE_CASE_ ) )
  
  
  
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							args
  
  
  
  
  
  
  
     def       a_							(							self ):
         warnings.warn(
             """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.""" )
         UpperCamelCase				:   int          =							os.path.abspath(self.args.path )
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             print("""This does not look like a valid git repo.""" )
             exit(1 )
         subprocess.run(
             """git config lfs.customtransfer.multipart.path transformers-cli""".split()    ,      check=SCREAMING_SNAKE_CASE_    ,      cwd=SCREAMING_SNAKE_CASE_ )
         subprocess.run(
             f'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()    ,      check=SCREAMING_SNAKE_CASE_    ,      cwd=SCREAMING_SNAKE_CASE_    ,      )
         print("""Local repo set up for largefiles""" )
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							json.dumps(snake_case_    ) + """\n"""
      sys.stdout.write(snake_case_    )
      sys.stdout.flush()
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							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 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							filepath
         UpperCamelCase				:   Optional[Any]          =							seek_from
         UpperCamelCase				:   List[Any]          =							read_limit
         UpperCamelCase				:   Tuple          =							0
  
  
  
  
  
  
  
     def __enter__(							self ):
         UpperCamelCase				:   Optional[Any]          =							open(self.filepath    ,      """rb""" )
         self.f.seek(self.seek_from )
         return self
  
  
  
  
  
  
  
     def __len__(							self ):
         UpperCamelCase				:   Dict          =							os.fstat(self.f.fileno() ).st_size
         return min(self.read_limit    ,      total_length - self.seek_from )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=-1 ):
         if self.n_seen >= self.read_limit:
             return b""
         UpperCamelCase				:   List[str]          =							self.read_limit - self.n_seen
         UpperCamelCase				:   Optional[int]          =							self.f.read(remaining_amount if n < 0 else min(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
         self.n_seen += len(SCREAMING_SNAKE_CASE_ )
         return data
  
  
  
  
  
  
  
     def __iter__(							self ):
         yield self.read(n=4 * 1024 * 1024 )
  
  
  
  
  
  
  
     def __exit__(							self    ,      *SCREAMING_SNAKE_CASE_ ):
         self.f.close()
  
  
  
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   int          =							args
  
  
  
  
  
  
  
     def       a_							(							self ):
         # Immediately after invoking a custom transfer process, git-lfs
         # sends initiation data to the process over stdin.
         # This tells the process useful information about the configuration.
         UpperCamelCase				:   List[Any]          =							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:
             UpperCamelCase				:   Dict          =							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 )
  
             UpperCamelCase				:   List[Any]          =							msg["""oid"""]
             UpperCamelCase				:   str          =							msg["""path"""]
             UpperCamelCase				:   str          =							msg["""action"""]["""href"""]
             UpperCamelCase				:   Dict          =							msg["""action"""]["""header"""]
             UpperCamelCase				:   Optional[Any]          =							int(header.pop("""chunk_size""" ) )
             UpperCamelCase				:   List[str]          =							list(header.values() )
  
             UpperCamelCase				:   Tuple          =							[]
             for i, presigned_url in enumerate(SCREAMING_SNAKE_CASE_ ):
                 with FileSlice(SCREAMING_SNAKE_CASE_    ,      seek_from=i * chunk_size    ,      read_limit=SCREAMING_SNAKE_CASE_ ) as data:
                     UpperCamelCase				:   Optional[Any]          =							requests.put(SCREAMING_SNAKE_CASE_    ,      data=SCREAMING_SNAKE_CASE_ )
                     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.
  
             UpperCamelCase				:   Tuple          =							requests.post(
                 SCREAMING_SNAKE_CASE_    ,      json={
                     """oid""": oid,
                     """parts""": parts,
                 }    ,      )
             r.raise_for_status()
  
             write_msg({"""event""": """complete""", """oid""": oid} )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  __A :		Optional[Any]      							=    getLogger(__name__)
  
  
  __A :		Any      							=    4
  __A :		str      							=    7
  
  __A :		Any      							=    {
      '''float16''': torch.floataa,
      '''float32''': torch.floataa,
  }
  def 					A_			(		snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,torch.Tensor    ):
          return image
      elif isinstance(snake_case_						,PIL.Image.Image    ):
          UpperCamelCase				:   List[Any]          =							[image]
  
      if isinstance(image[0]						,PIL.Image.Image    ):
          UpperCamelCase       ,							UpperCamelCase				:   Any          =							image[0].size
          UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32
  
          UpperCamelCase				:   Tuple          =							[np.array(i.resize((w, h)    )    )[None, :] for i in image]
          UpperCamelCase				:   Union[str, Any]          =							np.concatenate(snake_case_						,axis=0    )
          UpperCamelCase				:   Union[str, Any]          =							np.array(snake_case_    ).astype(np.floataa    ) / 255.0
          UpperCamelCase				:   Dict          =							image.transpose(0						,3						,1						,2    )
          UpperCamelCase				:   Tuple          =							2.0 * image - 1.0
          UpperCamelCase				:   Tuple          =							torch.from_numpy(snake_case_    )
      elif isinstance(image[0]						,torch.Tensor    ):
          UpperCamelCase				:   Union[str, Any]          =							torch.cat(snake_case_						,dim=0    )
  
      return image
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 350    ,      ):
         super().__init__(
             vae=SCREAMING_SNAKE_CASE_    ,      text_encoder=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      unet=SCREAMING_SNAKE_CASE_    ,      low_res_scheduler=SCREAMING_SNAKE_CASE_    ,      scheduler=SCREAMING_SNAKE_CASE_    ,      safety_checker=SCREAMING_SNAKE_CASE_    ,      feature_extractor=SCREAMING_SNAKE_CASE_    ,      watermarker=SCREAMING_SNAKE_CASE_    ,      max_noise_level=SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 75    ,      SCREAMING_SNAKE_CASE_ = 9.0    ,      SCREAMING_SNAKE_CASE_ = 20    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "pil"    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      ):
         self.check_inputs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         # 2. Define call parameters
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Tuple          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         UpperCamelCase				:   Optional[int]          =							self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCamelCase				:   str          =							guidance_scale > 1.0
  
         # 3. Encode input prompt
         UpperCamelCase				:   Dict          =							self._encode_prompt(
             SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      prompt_embeds=SCREAMING_SNAKE_CASE_    ,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   Optional[int]          =							ORT_TO_PT_TYPE[str(text_embeddings.dtype )]
  
         # 4. Preprocess image
         UpperCamelCase				:   List[Any]          =							preprocess(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							image.cpu()
  
         # 5. set timesteps
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[str]          =							self.scheduler.timesteps
  
         # 5. Add noise to image
         UpperCamelCase				:   Union[str, Any]          =							torch.tensor([noise_level]    ,      dtype=torch.long    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							torch.randn(image.shape    ,      generator=SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Dict          =							2 if do_classifier_free_guidance else 1
         UpperCamelCase				:   List[Any]          =							np.concatenate([image] * batch_multiplier * num_images_per_prompt )
         UpperCamelCase				:   Any          =							np.concatenate([noise_level] * image.shape[0] )
  
         # 6. Prepare latent variables
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							image.shape[2:]
         UpperCamelCase				:   Any          =							self.prepare_latents(
             batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      )
  
         # 7. Check that sizes of image and latents match
         UpperCamelCase				:   Any          =							image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                 f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
                 f' `num_channels_image`: {num_channels_image} '
                 f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
                 """ `pipeline.unet` or your `image` input.""" )
  
         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         UpperCamelCase				:   Union[str, Any]          =							self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[int]          =							next(
             (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")    ,      """tensor(float)""" )
         UpperCamelCase				:   Tuple          =							ORT_TO_NP_TYPE[timestep_dtype]
  
         # 9. Denoising loop
         UpperCamelCase				:   Optional[Any]          =							len(SCREAMING_SNAKE_CASE_ ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=SCREAMING_SNAKE_CASE_ ) as progress_bar:
             for i, t in enumerate(SCREAMING_SNAKE_CASE_ ):
                 # expand the latents if we are doing classifier free guidance
                 UpperCamelCase				:   int          =							np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents
  
                 # concat latents, mask, masked_image_latents in the channel dimension
                 UpperCamelCase				:   Dict          =							self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   List[str]          =							np.concatenate([latent_model_input, image]    ,      axis=1 )
  
                 # timestep to tensor
                 UpperCamelCase				:   Any          =							np.array([t]    ,      dtype=SCREAMING_SNAKE_CASE_ )
  
                 # predict the noise residual
                 UpperCamelCase				:   List[Any]          =							self.unet(
                     sample=SCREAMING_SNAKE_CASE_    ,      timestep=SCREAMING_SNAKE_CASE_    ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_    ,      class_labels=noise_level.astype(np.intaa )    ,      )[0]
  
                 # perform guidance
                 if do_classifier_free_guidance:
                     UpperCamelCase       ,							UpperCamelCase				:   Dict          =							np.split(SCREAMING_SNAKE_CASE_    ,      2 )
                 UpperCamelCase				:   Tuple          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase				:   Tuple          =							self.scheduler.step(
                     torch.from_numpy(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ).prev_sample
  
                 # call the callback, if provided
                 if i == len(SCREAMING_SNAKE_CASE_ ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
          # 10. Post-processing
         UpperCamelCase				:   List[str]          =							self.decode_latents(latents.float() )
  
         # 11. Convert to PIL
         if output_type == "pil":
             UpperCamelCase				:   str          =							self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
  
         if not return_dict:
             return (image,)
  
         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							1 / 0.08333 * latents
         UpperCamelCase				:   Union[str, Any]          =							self.vae(latent_sample=SCREAMING_SNAKE_CASE_ )[0]
         UpperCamelCase				:   Union[str, Any]          =							np.clip(image / 2 + 0.5    ,      0    ,      1 )
         UpperCamelCase				:   Any          =							image.transpose((0, 2, 3, 1) )
         return image
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   List[Any]          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Dict          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         if prompt_embeds is None:
             UpperCamelCase				:   List[Any]          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
             UpperCamelCase				:   int          =							text_inputs.input_ids
             UpperCamelCase				:   List[Any]          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      padding="""longest"""    ,      return_tensors="""pt""" ).input_ids
  
             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   Dict          =							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}' )
  
             # no positional arguments to text_encoder
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   str          =							prompt_embeds[0]
  
         UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase				:   Tuple          =							prompt_embeds.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -1 )
  
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             UpperCamelCase				:   List[str]
             if negative_prompt is None:
                 UpperCamelCase				:   Optional[int]          =							[""""""] * batch_size
             elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
                 raise TypeError(
                     f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='
                     f' {type(SCREAMING_SNAKE_CASE_ )}.' )
             elif isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   List[Any]          =							[negative_prompt]
             elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
                 raise ValueError(
                     f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'
                     f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                     """ the batch size of `prompt`.""" )
             else:
                 UpperCamelCase				:   Optional[Any]          =							negative_prompt
  
             UpperCamelCase				:   Union[str, Any]          =							text_input_ids.shape[-1]
             UpperCamelCase				:   Tuple          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=SCREAMING_SNAKE_CASE_    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
  
             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None
  
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   Tuple          =							uncond_embeddings[0]
  
         if do_classifier_free_guidance:
             UpperCamelCase				:   Dict          =							uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCamelCase				:   Tuple          =							uncond_embeddings.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[str]          =							uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -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
             UpperCamelCase				:   Any          =							np.concatenate([uncond_embeddings, prompt_embeds] )
  
         return prompt_embeds
  
  
  
  
  