






from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

SCREAMING_SNAKE_CASE 							=						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'

def      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							Union[str, Any]		,		__A    :							str=8 )      ->					Optional[int]:

    '''simple docstring'''





    _UpperCAmelCase				:      str			  =				h // scale_factor**2
    if h % scale_factor**2 != 0:
        new_h += 1
    _UpperCAmelCase				:      List[Any]			  =				w // scale_factor**2
    if w % scale_factor**2 != 0:
        new_w += 1
    return new_h * scale_factor, new_w * scale_factor


class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A							,  _A							,  _A							,  _A							,  ) ->		Dict:

     """simple docstring"""



     super().__init__()

     self.register_modules(
         text_encoder=_A							,  tokenizer=_A							,  unet=_A							,  scheduler=_A							,  movq=_A							,  )
     _UpperCAmelCase				:      Any			  =				2 ** (len(self.movq.config.block_out_channels) - 1)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A							,  _A) ->		List[str]:

     """simple docstring"""



     if latents is None:
         _UpperCAmelCase				:      List[str]			  =				randn_tensor(_A							,  generator=_A							,  device=_A							,  dtype=_A)
     else:
         if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
         _UpperCAmelCase				:      Optional[Any]			  =				latents.to(_A)

     _UpperCAmelCase				:      List[Any]			  =				latents * scheduler.init_noise_sigma
     return latents


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  ) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				len(_A) if isinstance(_A							,  _A) else 1
     # get prompt text embeddings
     _UpperCAmelCase				:      Optional[int]			  =				self.tokenizer(
         _A							,  padding='''max_length'''							,  truncation=_A							,  max_length=77							,  return_attention_mask=_A							,  add_special_tokens=_A							,  return_tensors='''pt'''							,  )

     _UpperCAmelCase				:      List[str]			  =				text_inputs.input_ids
     _UpperCAmelCase				:      List[Any]			  =				self.tokenizer(_A							,  padding='''longest'''							,  return_tensors='''pt''').input_ids

     if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_A							,  _A):
         _UpperCAmelCase				:      List[str]			  =				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}''')

     _UpperCAmelCase				:      Union[str, Any]			  =				text_input_ids.to(_A)
     _UpperCAmelCase				:      List[Any]			  =				text_inputs.attention_mask.to(_A)

     _UpperCAmelCase				:      Optional[Any]			  =				self.text_encoder(
         input_ids=_A							,  attention_mask=_A)

     _UpperCAmelCase				:      str			  =				prompt_embeds.repeat_interleave(_A							,  dim=0)
     _UpperCAmelCase				:      str			  =				text_encoder_hidden_states.repeat_interleave(_A							,  dim=0)
     _UpperCAmelCase				:      Dict			  =				text_mask.repeat_interleave(_A							,  dim=0)

     if do_classifier_free_guidance:
         _UpperCAmelCase				:      List[str]
         if negative_prompt is None:
             _UpperCAmelCase				:      Optional[int]			  =				[''''''] * batch_size
         elif type(_A) is not type(_A):
             raise TypeError(
                 f'''`negative_prompt` should be the same type to `prompt`, but got {type(_A)} !='''
                 f''' {type(_A)}.''')
         elif isinstance(_A							,  _A):
             _UpperCAmelCase				:      List[str]			  =				[negative_prompt]
         elif batch_size != len(_A):
             raise ValueError(
                 f'''`negative_prompt`: {negative_prompt} has batch size {len(_A)}, but `prompt`:'''
                 f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                 ''' the batch size of `prompt`.''')
         else:
             _UpperCAmelCase				:      Union[str, Any]			  =				negative_prompt

         _UpperCAmelCase				:      Tuple			  =				self.tokenizer(
             _A							,  padding='''max_length'''							,  max_length=77							,  truncation=_A							,  return_attention_mask=_A							,  add_special_tokens=_A							,  return_tensors='''pt'''							,  )
         _UpperCAmelCase				:      List[Any]			  =				uncond_input.input_ids.to(_A)
         _UpperCAmelCase				:      str			  =				uncond_input.attention_mask.to(_A)

         _UpperCAmelCase				:      Tuple			  =				self.text_encoder(
             input_ids=_A							,  attention_mask=_A)

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

         _UpperCAmelCase				:      List[str]			  =				negative_prompt_embeds.shape[1]
         _UpperCAmelCase				:      List[str]			  =				negative_prompt_embeds.repeat(1							,  _A)
         _UpperCAmelCase				:      Union[str, Any]			  =				negative_prompt_embeds.view(batch_size * num_images_per_prompt							,  _A)

         _UpperCAmelCase				:      int			  =				uncond_text_encoder_hidden_states.shape[1]
         _UpperCAmelCase				:      Dict			  =				uncond_text_encoder_hidden_states.repeat(1							,  _A							,  1)
         _UpperCAmelCase				:      Optional[int]			  =				uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt							,  _A							,  -1)
         _UpperCAmelCase				:      List[str]			  =				uncond_text_mask.repeat_interleave(_A							,  dim=0)

         # done duplicates

         # 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])
         _UpperCAmelCase				:      List[Any]			  =				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

         _UpperCAmelCase				:      Optional[int]			  =				torch.cat([uncond_text_mask, text_mask])

     return prompt_embeds, text_encoder_hidden_states, text_mask


 def  snake_case__				(	self							,  _A=0) ->		List[str]:

     """simple docstring"""



     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('''Please install accelerate via `pip install accelerate`''')

     _UpperCAmelCase				:      Dict			  =				torch.device(f'''cuda:{gpu_id}''')

     _UpperCAmelCase				:      str			  =				[
         self.unet,
         self.text_encoder,
         self.movq,
     ]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(_A							,  _A)


 def  snake_case__				(	self							,  _A=0) ->		int:

     """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.''')

     _UpperCAmelCase				:      str			  =				torch.device(f'''cuda:{gpu_id}''')

     if self.device.type != "cpu":
         self.to('''cpu'''							,  silence_dtype_warnings=_A)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     _UpperCAmelCase				:      int			  =				None
     for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
         _UpperCAmelCase				:      Tuple			  =				cpu_offload_with_hook(_A							,  _A							,  prev_module_hook=_A)

     if self.safety_checker is not None:
         _UpperCAmelCase				:      int			  =				cpu_offload_with_hook(self.safety_checker							,  _A							,  prev_module_hook=_A)

     # We'll offload the last model manually.
     _UpperCAmelCase				:      Any			  =				hook


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     if not hasattr(self.unet							,  '''_hf_hook'''):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(_A							,  '''_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


 @torch.no_grad()
 @replace_example_docstring(_A)
 def __call__(	self							,  _A							,  _A							,  _A							,  _A = None							,  _A = 512							,  _A = 512							,  _A = 100							,  _A = 4.0							,  _A = 1							,  _A = None							,  _A = None							,  _A = "pil"							,  _A = True							,  ) ->		str:

     """simple docstring"""



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

     _UpperCAmelCase				:      str			  =				self._execution_device

     _UpperCAmelCase				:      List[Any]			  =				batch_size * num_images_per_prompt
     _UpperCAmelCase				:      int			  =				guidance_scale > 1.0

     _UpperCAmelCase				:      List[str]			  =				self._encode_prompt(
         _A							,  _A							,  _A							,  _A							,  _A)

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      int			  =				torch.cat(_A							,  dim=0)
     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				torch.cat(_A							,  dim=0)

     if do_classifier_free_guidance:
         _UpperCAmelCase				:      int			  =				image_embeds.repeat_interleave(_A							,  dim=0)
         _UpperCAmelCase				:      int			  =				negative_image_embeds.repeat_interleave(_A							,  dim=0)

     _UpperCAmelCase				:      Union[str, Any]			  =				torch.cat([negative_image_embeds, image_embeds]							,  dim=0).to(
         dtype=prompt_embeds.dtype							,  device=_A)

     self.scheduler.set_timesteps(_A							,  device=_A)
     _UpperCAmelCase				:      Dict			  =				self.scheduler.timesteps

     _UpperCAmelCase				:      Tuple			  =				self.unet.config.in_channels

     _UpperCAmelCase				:      List[str]			  =				get_new_h_w(_A							,  _A							,  self.movq_scale_factor)

     # create initial latent
     _UpperCAmelCase				:      Optional[Any]			  =				self.prepare_latents(
         (batch_size, num_channels_latents, height, width)							,  text_encoder_hidden_states.dtype							,  _A							,  _A							,  _A							,  self.scheduler							,  )

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

         _UpperCAmelCase				:      Union[str, Any]			  =				{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
         _UpperCAmelCase				:      List[str]			  =				self.unet(
             sample=_A							,  timestep=_A							,  encoder_hidden_states=_A							,  added_cond_kwargs=_A							,  return_dict=_A							,  )[0]

         if do_classifier_free_guidance:
             _UpperCAmelCase				:      Tuple			  =				noise_pred.split(latents.shape[1]							,  dim=1)
             _UpperCAmelCase				:      int			  =				noise_pred.chunk(2)
             _UpperCAmelCase				:      List[Any]			  =				variance_pred.chunk(2)
             _UpperCAmelCase				:      Optional[Any]			  =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             _UpperCAmelCase				:      str			  =				torch.cat([noise_pred, variance_pred_text]							,  dim=1)

         if not (
             hasattr(self.scheduler.config							,  '''variance_type''')
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
             _UpperCAmelCase				:      Tuple			  =				noise_pred.split(latents.shape[1]							,  dim=1)

         # compute the previous noisy sample x_t -> x_t-1
         _UpperCAmelCase				:      int			  =				self.scheduler.step(
             _A							,  _A							,  _A							,  generator=_A							,  ).prev_sample
     # post-processing
     _UpperCAmelCase				:      int			  =				self.movq.decode(_A							,  force_not_quantize=_A)['''sample''']

     if output_type not in ["pt", "np", "pil"]:
         raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''')

     if output_type in ["np", "pil"]:
         _UpperCAmelCase				:      int			  =				image * 0.5 + 0.5
         _UpperCAmelCase				:      Optional[int]			  =				image.clamp(0							,  1)
         _UpperCAmelCase				:      Tuple			  =				image.cpu().permute(0							,  2							,  3							,  1).float().numpy()

     if output_type == "pil":
         _UpperCAmelCase				:      Optional[int]			  =				self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)












import argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE 							=						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE 							=						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				len(re.search(r'''^(\s*)\S'''		,		lines[line_idx] ).groups()[0] ) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
                new_lines.append(lines[line_idx] )
                line_idx += 1

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
                else:
                    blocks.append(lines[line_idx] )
                line_idx += 1

            # Sort blocks by their identifiers
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

    if overwrite:
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            f.write('''\n'''.join(__A ) )
    elif "\n".join(__A ) != content:
        return True

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




