




"""simple docstring"""



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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




class      UpperCAmelCase_  (						A_,       A_							):




    @register_to_config
    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	bool     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None				)				->							List[Any]:




     '''simple docstring'''

     super().__init__()

     A__	=    learnable

     if self.learnable:
      assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
      assert length is not None, "learnable=True requires `length` to be set"

      A__	=    torch.zeros(snake_case_     ,			snake_case_				)
     else:
      A__	=    None

     A__	=    torch.nn.Parameter(snake_case_				)






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				42
    lowercase__						 =				42
    lowercase__						 =				42
    lowercase__						 =				42
    lowercase__						 =				42
    lowercase__						 =				42




    def __init__(   self						:	List[str]     ,			snake_case_						:	VQModel     ,			snake_case_						:	CLIPTextModel     ,			snake_case_						:	CLIPTokenizer     ,			snake_case_						:	TransformeraDModel     ,			snake_case_						:	VQDiffusionScheduler     ,			snake_case_						:	LearnedClassifierFreeSamplingEmbeddings     ,			)				->							Any:




     '''simple docstring'''

     super().__init__()

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




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	List[str]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any				)				->							Dict:




     '''simple docstring'''

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

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

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
      A__	=    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}"""				)
      A__	=    text_input_ids[:, : self.tokenizer.model_max_length]
     A__	=    self.text_encoder(text_input_ids.to(self.device				)				)[0]

     # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
     # While CLIP does normalize the pooled output of the text transformer when combining
     # the image and text embeddings, CLIP does not directly normalize the last hidden state.
     #
     # CLIP normalizing the pooled output.
     # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
     A__	=    prompt_embeds / prompt_embeds.norm(dim=-1     ,			keepdim=snake_case_				)

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

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

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

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

     return prompt_embeds




    @torch.no_grad()
    def __call__(   self						:	Union[str, Any]     ,			snake_case_						:	Union[str, List[str]]     ,			snake_case_						:	int = 100     ,			snake_case_						:	float = 5.0     ,			snake_case_						:	float = 1.0     ,			snake_case_						:	int = 1     ,			snake_case_						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,			snake_case_						:	Optional[torch.FloatTensor] = None     ,			snake_case_						:	Optional[str] = "pil"     ,			snake_case_						:	bool = True     ,			snake_case_						:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,			snake_case_						:	int = 1     ,			)				->							Union[ImagePipelineOutput, Tuple]:




     '''simple docstring'''

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

     A__	=    batch_size * num_images_per_prompt

     A__	=    guidance_scale > 1.0

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

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

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

     A__	=    (batch_size, self.transformer.num_latent_pixels)
     if latents is None:
      A__	=    self.transformer.num_vector_embeds - 1
      A__	=    torch.full(snake_case_     ,			snake_case_				).to(self.device				)
     else:
      if latents.shape != latents_shape:
       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
      if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
       raise ValueError(
           "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
           F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""				)
      A__	=    latents.to(self.device				)

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

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

     A__	=    latents

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

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

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

      A__	=    self.truncate(snake_case_     ,			snake_case_				)

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

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

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

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

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

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

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	torch.FloatTensor     ,			snake_case_						:	float				)				->							torch.FloatTensor:




     '''simple docstring'''

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

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

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

     A__	=    log_p_x_0.clone()

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

     return rv











"""simple docstring"""



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

 parser.add_argument(
     "--config_file"      ,			default=lowercase_      ,			help="The config file to use for the default values in the launching script."						)

 if subparsers is not None:
  parser.set_defaults(func=lowercase_						)
 return parser







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`Accelerate` version": version,
     "Platform": platform.platform(),
     "Python version": platform.python_version(),
     "Numpy version": np.__version__,
     "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
     "PyTorch XPU available": str(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    torch.cuda.get_device_name()

 print("\nCopy-and-paste the text below in your GitHub issue\n"						)
 print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

 print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


if __name__ == "__main__":
      raise SystemExit(main())





