'''simple docstring'''

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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_lowerCamelCase :				List[str]														= logging.get_logger(__name__)  # pylint: disable=invalid-name



def  _lowerCAmelCase				(				self			,      __a			,      __a			,      __a			,      __a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =image.to(device=__a      )
     _UpperCamelCase  :Union[str, Any]       =image  # take image as image_embeddings
     _UpperCamelCase  :Dict       =image_embeddings.unsqueeze(1      )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :str       =image_embeddings.shape
     _UpperCamelCase  :int       =image_embeddings.repeat(1			,      __a			,      1      )
     _UpperCamelCase  :int       =image_embeddings.view(bs_embed * num_images_per_prompt			,      __a			,      -1      )

     if do_classifier_free_guidance:
          _UpperCamelCase  :str       =torch.zeros_like(__a      )

          # 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  :List[Any]       =torch.cat([uncond_embeddings, image_embeddings]      )

     return image_embeddings




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Union[str, Any]:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :Any       ={"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        _UpperCamelCase  :List[Any]       =decoder_pipe_kwargs.get("""torch_dtype"""					,      lowerCAmelCase__     ) or prior.dtype

        _UpperCamelCase  :Union[str, Any]       =StableDiffusionImageVariationPipeline.from_pretrained(
            """lambdalabs/sd-image-variations-diffusers"""					,      **lowerCAmelCase__     )

        # replace `_encode_image` method
        _UpperCamelCase  :Tuple       =types.MethodType(_encode_image					,      self.decoder_pipe     )

        self.register_modules(
            prior=lowerCAmelCase__					,      tokenizer=lowerCAmelCase__					,      text_encoder=lowerCAmelCase__					,      prior_scheduler=lowerCAmelCase__					,      )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     str:
        """simple docstring"""





        if text_model_output is None:
             _UpperCamelCase  :Optional[int]       =len(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ) else 1
             # get prompt text embeddings
             _UpperCamelCase  :Any       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :List[str]       =text_inputs.input_ids
             _UpperCamelCase  :Tuple       =text_inputs.attention_mask.bool().to(lowerCAmelCase__     )

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  _UpperCamelCase  :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  :Tuple       =text_input_ids[:, : self.tokenizer.model_max_length]

             _UpperCamelCase  :str       =self.text_encoder(text_input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :str       =text_encoder_output.text_embeds
             _UpperCamelCase  :Any       =text_encoder_output.last_hidden_state

        else:
             _UpperCamelCase  :Dict       =text_model_output[0].shape[0]
             _UpperCamelCase       ,      _UpperCamelCase  :Dict       =text_model_output[0], text_model_output[1]
             _UpperCamelCase  :Tuple       =text_attention_mask

        _UpperCamelCase  :Optional[Any]       =text_embeddings.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :Optional[int]       =text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :List[Any]       =text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

        if do_classifier_free_guidance:
             _UpperCamelCase  :Optional[int]       =[""""""] * batch_size

             _UpperCamelCase  :Dict       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :Any       =uncond_input.attention_mask.bool().to(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :Optional[int]       =uncond_embeddings_text_encoder_output.text_embeds
             _UpperCamelCase  :Optional[Any]       =uncond_embeddings_text_encoder_output.last_hidden_state

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

             _UpperCamelCase  :List[Any]       =uncond_embeddings.shape[1]
             _UpperCamelCase  :Dict       =uncond_embeddings.repeat(1					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =uncond_embeddings.view(batch_size * num_images_per_prompt					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.shape[1]
             _UpperCamelCase  :List[Any]       =uncond_text_encoder_hidden_states.repeat(1					,      lowerCAmelCase__					,      1     )
             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt					,      lowerCAmelCase__					,      -1     )
             _UpperCamelCase  :Tuple       =uncond_text_mask.repeat_interleave(lowerCAmelCase__					,      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  :Any       =torch.cat([uncond_embeddings, text_embeddings]     )
             _UpperCamelCase  :Any       =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

             _UpperCamelCase  :Dict       =torch.cat([uncond_text_mask, text_mask]     )

        return text_embeddings, text_encoder_hidden_states, text_mask



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        if self.device != torch.device("""meta"""     ) or not hasattr(self.prior					,      """_hf_hook"""     ):
             return self.device
        for module in self.prior.modules():
             if (
                 hasattr(lowerCAmelCase__					,      """_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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





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

        _UpperCamelCase  :Any       =latents * scheduler.init_noise_sigma
        return latents



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__ = None     )				->     int:
        """simple docstring"""





        self.decoder_pipe.to(lowerCAmelCase__     )
        super().to(lowerCAmelCase__     )




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 25					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 4.0					,      lowerCAmelCase__ = 8.0					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 0.0					,      lowerCAmelCase__ = "pil"					,      lowerCAmelCase__ = True					,      )				->     Dict:
        """simple docstring"""





        if prompt is not None:
             if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =1
             elif isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =len(lowerCAmelCase__     )
             else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )
        else:
             _UpperCamelCase  :int       =text_model_output[0].shape[0]

        _UpperCamelCase  :List[str]       =self._execution_device

        _UpperCamelCase  :List[str]       =batch_size * num_images_per_prompt

        _UpperCamelCase  :Optional[Any]       =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._encode_prompt(
            lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__					,      device=lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =self.prior_scheduler.timesteps

        _UpperCamelCase  :int       =self.prior.config.embedding_dim

        _UpperCamelCase  :Union[str, Any]       =self.prepare_latents(
            (batch_size, embedding_dim)					,      text_embeddings.dtype					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      self.prior_scheduler					,      )

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

             _UpperCamelCase  :Dict       =self.prior(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      proj_embedding=lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__					,      attention_mask=lowerCAmelCase__					,      ).predicted_image_embedding

             if do_classifier_free_guidance:
                  _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =predicted_image_embedding.chunk(2     )
                  _UpperCamelCase  :Dict       =predicted_image_embedding_uncond + prior_guidance_scale * (
                      predicted_image_embedding_text - predicted_image_embedding_uncond
                  )

             if i + 1 == prior_timesteps_tensor.shape[0]:
                  _UpperCamelCase  :Tuple       =None
             else:
                  _UpperCamelCase  :Optional[int]       =prior_timesteps_tensor[i + 1]

             _UpperCamelCase  :Tuple       =self.prior_scheduler.step(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      sample=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      prev_timestep=lowerCAmelCase__					,      ).prev_sample

        _UpperCamelCase  :Any       =self.prior.post_process_latents(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =prior_latents

        _UpperCamelCase  :Tuple       =self.decoder_pipe(
            image=lowerCAmelCase__					,      height=lowerCAmelCase__					,      width=lowerCAmelCase__					,      num_inference_steps=lowerCAmelCase__					,      guidance_scale=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      output_type=lowerCAmelCase__					,      return_dict=lowerCAmelCase__					,      num_images_per_prompt=lowerCAmelCase__					,      eta=lowerCAmelCase__					,      )
        return output







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a       =    '''src/transformers'''

# Matches is_xxx_available()
_a       =    re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
_a       =    re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
_a       =    re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


_a       =    '''
{0} = None
'''

_a       =    '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


_a       =    '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def __A       ( __lowerCAmelCase      )->    str:





						"""simple docstring"""



						if _re_test_backend.search(__lowerCAmelCase      ) is None:
												return None
						_UpperCAmelCase							   =     [b[0] for b in _re_backend.findall(__lowerCAmelCase      )]
						backends.sort()
						return "_and_".join(__lowerCAmelCase      )


def __A       ( )->    int:





						"""simple docstring"""



						with open(os.path.join(__lowerCAmelCase					,						'__init__.py'      )					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
												_UpperCAmelCase							   =     f.readlines()

						# Get to the point we do the actual imports for type checking
						_UpperCAmelCase							   =     0
						while not lines[line_index].startswith('if TYPE_CHECKING'      ):
												line_index += 1

						_UpperCAmelCase							   =     {}
						# Go through the end of the file
						while line_index < len(__lowerCAmelCase      ):
												# If the line is an if is_backend_available, we grab all objects associated.
												_UpperCAmelCase							   =     find_backend(lines[line_index]      )
												if backend is not None:
																		while not lines[line_index].startswith('    else:'      ):
																								line_index += 1
																		line_index += 1

																		_UpperCAmelCase							   =     []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(' ' * 8      ):
																								_UpperCAmelCase							   =     lines[line_index]
																								_UpperCAmelCase							   =     _re_single_line_import.search(__lowerCAmelCase      )
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(', '      )      )
																								elif line.startswith(' ' * 12      ):
																														objects.append(line[12:-2]      )
																								line_index += 1

																		_UpperCAmelCase							   =     objects
												else:
																		line_index += 1

						return backend_specific_objects


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(__lowerCAmelCase      )
						elif name.islower():
												return DUMMY_FUNCTION.format(__lowerCAmelCase					,						__lowerCAmelCase      )
						else:
												return DUMMY_CLASS.format(__lowerCAmelCase					,						__lowerCAmelCase      )


def __A       ( __lowerCAmelCase=None      )->    Union[str, Any]:





						"""simple docstring"""



						if backend_specific_objects is None:
												_UpperCAmelCase							   =     read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						_UpperCAmelCase							   =     {}

						for backend, objects in backend_specific_objects.items():
												_UpperCAmelCase							   =     '[' + ', '.join(F"""\"{b}\"""" for b in backend.split('_and_'      )      ) + ']'
												_UpperCAmelCase							   =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(__lowerCAmelCase					,						__lowerCAmelCase      ) for o in objects]      )
												_UpperCAmelCase							   =     dummy_file

						return dummy_files


def __A       ( __lowerCAmelCase=False      )->    int:





						"""simple docstring"""



						_UpperCAmelCase							   =     create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						_UpperCAmelCase							   =     {'torch': 'pt'}

						# Locate actual dummy modules and read their content.
						_UpperCAmelCase							   =     os.path.join(__lowerCAmelCase					,						'utils'      )
						_UpperCAmelCase							   =     {
						    backend: os.path.join(__lowerCAmelCase					,						F"""dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py"""      )
						    for backend in dummy_files.keys()
						}

						_UpperCAmelCase							   =     {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(__lowerCAmelCase      ):
																		with open(__lowerCAmelCase					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																								_UpperCAmelCase							   =     f.read()
												else:
																		_UpperCAmelCase							   =     ''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"""Updating transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py as the main """
																								    '__init__ has new objects.'      )
																								with open(dummy_file_paths[backend]					,						'w'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																														f.write(dummy_files[backend]      )
																		else:
																								raise ValueError(
																								    'The main __init__ has objects that are not present in '
																								    F"""transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py. Run `make fix-copies` """
																								    'to fix this.'      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		_a       =    parser.parse_args()

		check_dummies(args.fix_and_overwrite)
