
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )

"""simple docstring"""



import argparse
import os
import re

import packaging.version


a					:	str							=		'''examples/'''
a					:	List[str]							=		{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a					:	Tuple							=		{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
a					:	List[str]							=		'''README.md'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase    ,_UpperCAmelCase          =   REPLACE_PATTERNS[pattern]
    _UpperCAmelCase          =   replace.replace("""VERSION"""					,      _A      )
    _UpperCAmelCase          =   re_pattern.sub(_A					,      _A      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.write(_A      )


def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    for folder, directories, fnames in os.walk(_A      ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""      )
        if "legacy" in directories:
            directories.remove("""legacy"""      )
        for fname in fnames:
            if fname.endswith(""".py"""      ):
                update_version_in_file(os.path.join(_A					,      _A      )					,      _A					,      pattern="""examples"""      )


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  int:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_A					,      _A					,      _A      )
    if not patch:
        update_version_in_examples(_A      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """🤗 Transformers currently provides the following architectures"""
    _UpperCAmelCase          =   """1. Want to contribute a new model?"""
    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.readlines()

    # Find the start of the list.
    _UpperCAmelCase          =   0
    while not lines[start_index].startswith(_start_prompt      ):
        start_index += 1
    start_index += 1

    _UpperCAmelCase          =   start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt      ):
        if lines[index].startswith("""1."""      ):
            _UpperCAmelCase          =   lines[index].replace(
                """https://huggingface.co/docs/diffusers/main/model_doc"""					,      """https://huggingface.co/docs/diffusers/model_doc"""					,      )
        index += 1

    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.writelines(_A      )


def  _UpperCamelCase	(						)    ->  Optional[int]:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]					,      """r"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase          =   REPLACE_PATTERNS["""init"""][0].search(_A      ).groups()[0]
    return packaging.version.parse(_A      )


def  _UpperCamelCase	(						_A=False      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
    if default_version.is_devrelease:
        _UpperCAmelCase          =   default_version.base_version
    elif patch:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor + 1}.0"""

    # Now let's ask nicely if that's the right one.
    _UpperCAmelCase          =   input(F"""Which version are you releasing? [{default_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   default_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A					,      patch=_A      )


def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    _UpperCAmelCase          =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _UpperCAmelCase          =   current_version.base_version

    # Check with the user we got that right.
    _UpperCAmelCase          =   input(F"""Which version are we developing now? [{dev_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   dev_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
 parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
 a					:	Tuple							=		parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print('''Nothing to do after a patch :-)''')
 else:
  post_release_work()