




"""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} )










"""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




