






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 							A_    (						__lowercase ,   __lowercase			):






 '''simple docstring'''




 @register_to_config
 def __init__(	self							,  _A							,  _A = None							,  _A = None) ->		Dict:

     """simple docstring"""



     super().__init__()

     _UpperCAmelCase				:      Union[str, Any]			  =				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"

         _UpperCAmelCase				:      List[Any]			  =				torch.zeros(_A							,  _A)
     else:
         _UpperCAmelCase				:      int			  =				None

     _UpperCAmelCase				:      List[str]			  =				torch.nn.Parameter(_A)



class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			VQModel
 _SCREAMING_SNAKE_CASE							:			CLIPTextModel
 _SCREAMING_SNAKE_CASE							:			CLIPTokenizer
 _SCREAMING_SNAKE_CASE							:			TransformeraDModel
 _SCREAMING_SNAKE_CASE							:			LearnedClassifierFreeSamplingEmbeddings
 _SCREAMING_SNAKE_CASE							:			VQDiffusionScheduler


 def __init__(	self							,  _A							,  _A							,  _A							,  _A							,  _A							,  _A							,  ) ->		List[Any]:

     """simple docstring"""



     super().__init__()

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


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				len(_A) if isinstance(_A							,  _A) else 1

     # get prompt text embeddings
     _UpperCAmelCase				:      int			  =				self.tokenizer(
         _A							,  padding='''max_length'''							,  max_length=self.tokenizer.model_max_length							,  return_tensors='''pt'''							,  )
     _UpperCAmelCase				:      int			  =				text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         _UpperCAmelCase				:      List[Any]			  =				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}''')
         _UpperCAmelCase				:      Any			  =				text_input_ids[:, : self.tokenizer.model_max_length]
     _UpperCAmelCase				:      List[Any]			  =				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
     _UpperCAmelCase				:      List[str]			  =				prompt_embeds / prompt_embeds.norm(dim=-1							,  keepdim=_A)

     # duplicate text embeddings for each generation per prompt
     _UpperCAmelCase				:      int			  =				prompt_embeds.repeat_interleave(_A							,  dim=0)

     if do_classifier_free_guidance:
         if self.learned_classifier_free_sampling_embeddings.learnable:
             _UpperCAmelCase				:      Tuple			  =				self.learned_classifier_free_sampling_embeddings.embeddings
             _UpperCAmelCase				:      List[Any]			  =				negative_prompt_embeds.unsqueeze(0).repeat(_A							,  1							,  1)
         else:
             _UpperCAmelCase				:      Any			  =				[''''''] * batch_size

             _UpperCAmelCase				:      Optional[int]			  =				text_input_ids.shape[-1]
             _UpperCAmelCase				:      Optional[int]			  =				self.tokenizer(
                 _A							,  padding='''max_length'''							,  max_length=_A							,  truncation=_A							,  return_tensors='''pt'''							,  )
             _UpperCAmelCase				:      Dict			  =				self.text_encoder(uncond_input.input_ids.to(self.device))[0]
             # See comment for normalizing text embeddings
             _UpperCAmelCase				:      Optional[int]			  =				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1							,  keepdim=_A)

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         _UpperCAmelCase				:      Any			  =				negative_prompt_embeds.shape[1]
         _UpperCAmelCase				:      Optional[Any]			  =				negative_prompt_embeds.repeat(1							,  _A							,  1)
         _UpperCAmelCase				:      str			  =				negative_prompt_embeds.view(batch_size * num_images_per_prompt							,  _A							,  -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				:      Tuple			  =				torch.cat([negative_prompt_embeds, prompt_embeds])

     return prompt_embeds


 @torch.no_grad()
 def __call__(	self							,  _A							,  _A = 100							,  _A = 5.0							,  _A = 1.0							,  _A = 1							,  _A = None							,  _A = None							,  _A = "pil"							,  _A = True							,  _A = None							,  _A = 1							,  ) ->		Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[str]			  =				1
     elif isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				len(_A)
     else:
         raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_A)}''')

     _UpperCAmelCase				:      Optional[Any]			  =				batch_size * num_images_per_prompt

     _UpperCAmelCase				:      List[Any]			  =				guidance_scale > 1.0

     _UpperCAmelCase				:      Optional[int]			  =				self._encode_prompt(_A							,  _A							,  _A)

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

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

     _UpperCAmelCase				:      Optional[Any]			  =				(batch_size, self.transformer.num_latent_pixels)
     if latents is None:
         _UpperCAmelCase				:      Union[str, Any]			  =				self.transformer.num_vector_embeds - 1
         _UpperCAmelCase				:      Union[str, Any]			  =				torch.full(_A							,  _A).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).''')
         _UpperCAmelCase				:      Any			  =				latents.to(self.device)

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

     _UpperCAmelCase				:      Dict			  =				self.scheduler.timesteps.to(self.device)

     _UpperCAmelCase				:      Tuple			  =				latents

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

         # predict the un-noised image
         # model_output == `log_p_x_0`
         _UpperCAmelCase				:      Tuple			  =				self.transformer(_A							,  encoder_hidden_states=_A							,  timestep=_A).sample

         if do_classifier_free_guidance:
             _UpperCAmelCase  , _UpperCAmelCase				:      Optional[int]			  =				model_output.chunk(2)
             _UpperCAmelCase				:      Optional[Any]			  =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
             model_output -= torch.logsumexp(_A							,  dim=1							,  keepdim=_A)

         _UpperCAmelCase				:      List[Any]			  =				self.truncate(_A							,  _A)

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

         # compute the previous noisy sample x_t -> x_t-1
         _UpperCAmelCase				:      List[str]			  =				self.scheduler.step(_A							,  timestep=_A							,  sample=_A							,  generator=_A).prev_sample

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

     _UpperCAmelCase				:      Dict			  =				self.vqvae.config.vq_embed_dim
     _UpperCAmelCase				:      Optional[int]			  =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
     _UpperCAmelCase				:      Optional[int]			  =				self.vqvae.quantize.get_codebook_entry(_A							,  shape=_A)
     _UpperCAmelCase				:      List[str]			  =				self.vqvae.decode(_A							,  force_not_quantize=_A).sample

     _UpperCAmelCase				:      List[Any]			  =				(image / 2 + 0.5).clamp(0							,  1)
     _UpperCAmelCase				:      str			  =				image.cpu().permute(0							,  2							,  3							,  1).numpy()

     if output_type == "pil":
         _UpperCAmelCase				:      List[str]			  =				self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)


 def  snake_case__				(	self							,  _A							,  _A) ->		torch.FloatTensor:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				torch.sort(_A							,  1							,  descending=_A)
     _UpperCAmelCase				:      List[Any]			  =				torch.exp(_A)
     _UpperCAmelCase				:      Any			  =				sorted_p_x_0.cumsum(dim=1) < truncation_rate

     # Ensure that at least the largest probability is not zeroed out
     _UpperCAmelCase				:      Dict			  =				torch.full_like(keep_mask[:, 0:1, :]							,  _A)
     _UpperCAmelCase				:      Tuple			  =				torch.cat((all_true, keep_mask)							,  dim=1)
     _UpperCAmelCase				:      Union[str, Any]			  =				keep_mask[:, :-1, :]

     _UpperCAmelCase				:      str			  =				keep_mask.gather(1							,  indices.argsort(1))

     _UpperCAmelCase				:      Tuple			  =				log_p_x_0.clone()

     _UpperCAmelCase				:      Any			  =				-torch.inf  # -inf = log(0)

     return rv












import os
from collections.abc import Iterator

def      _lowerCamelCase	(						__A    :							str = "." )      ->					Iterator[str]:
    for dir_path, dir_names, filenames in os.walk(__A ):
        _UpperCAmelCase				:      List[Any]			  =				[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(__A )[1] in (".py", ".ipynb"):
                yield os.path.join(__A		,		__A ).lstrip('''./''' )

def      _lowerCamelCase	(						__A    :							Dict )      ->					List[Any]:
    return f'''{i * '  '}*''' if i else "\n##"

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					str:
    _UpperCAmelCase				:      int			  =				old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(__A ) or old_parts[i] != new_part) and new_part:
            print(f'''{md_prefix(__A )} {new_part.replace('_'		,		' ' ).title()}''' )
    return new_path

def      _lowerCamelCase	(						__A    :							str = "." )      ->					None:
    _UpperCAmelCase				:      List[str]			  =				''''''
    for filepath in sorted(good_file_paths(__A ) ):
        _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				os.path.split(__A )
        if filepath != old_path:
            _UpperCAmelCase				:      Optional[int]			  =				print_path(__A		,		__A )
        _UpperCAmelCase				:      Dict			  =				(filepath.count(os.sep ) + 1) if filepath else 0
        _UpperCAmelCase				:      Union[str, Any]			  =				f'''{filepath}/{filename}'''.replace(''' '''		,		'''%20''' )
        _UpperCAmelCase				:      List[Any]			  =				os.path.splitext(filename.replace('''_'''		,		''' ''' ).title() )[0]
        print(f'''{md_prefix(__A )} [{filename}]({url})''' )


if __name__ == "__main__":
  print_directory_md('.')




