






import argparse
import os
import re

import packaging.version


__UpperCAmelCase								=						'''examples/'''
__UpperCAmelCase								=						{
    '''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'''),
}
__UpperCAmelCase								=						{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
__UpperCAmelCase								=						'''README.md'''




def      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							str     ,       __snake_case    :							Tuple  ):


					'''simple docstring'''


					with open(__a     ,       'r'     ,       encoding='utf-8'     ,       newline='\n'  ) as f:
										UpperCAmelCase_    :      List[str]            =						f.read()
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						REPLACE_PATTERNS[pattern]
					UpperCAmelCase_    :      int            =						replace.replace('VERSION'     ,       __a  )
					UpperCAmelCase_    :      List[str]            =						re_pattern.sub(__a     ,       __a  )
					with open(__a     ,       'w'     ,       encoding='utf-8'     ,       newline='\n'  ) as f:
										f.write(__a  )




def      lowercase__      (     __snake_case    :							int  ):


					'''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      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							Union[str, Any]=False  ):


					'''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      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						'🤗 Transformers currently provides the following architectures'
					UpperCAmelCase_    :      int            =						'1. Want to contribute a new model?'
					with open(__a     ,       'r'     ,       encoding='utf-8'     ,       newline='\n'  ) as f:
										UpperCAmelCase_    :      Optional[Any]            =						f.readlines()

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

					UpperCAmelCase_    :      Optional[Any]            =						start_index
					# Update the lines in the model list.
					while not lines[index].startswith(_end_prompt  ):
										if lines[index].startswith('1.'  ):
															UpperCAmelCase_    :      Union[str, Any]            =						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      lowercase__      (     ):


					'''simple docstring'''


					with open(REPLACE_FILES['init']     ,       'r'  ) as f:
										UpperCAmelCase_    :      int            =						f.read()
					UpperCAmelCase_    :      int            =						REPLACE_PATTERNS['init'][0].search(__a  ).groups()[0]
					return packaging.version.parse(__a  )




def      lowercase__      (     __snake_case    :							Any=False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						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_    :      Optional[Any]            =						default_version.base_version
					elif patch:
										UpperCAmelCase_    :      Optional[int]            =						F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
					else:
										UpperCAmelCase_    :      List[str]            =						F"{default_version.major}.{default_version.minor + 1}.0"

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

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




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						get_version()
					UpperCAmelCase_    :      Any            =						F"{current_version.major}.{current_version.minor + 1}.0.dev0"
					UpperCAmelCase_    :      Union[str, Any]            =						current_version.base_version

					# Check with the user we got that right.
					UpperCAmelCase_    :      int            =						input(F"Which version are we developing now? [{dev_version}]"  )
					if len(__a  ) == 0:
										UpperCAmelCase_    :      Any            =						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__":
		__UpperCAmelCase								=						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.')
		__UpperCAmelCase								=						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()













import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
					parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



