
import argparse
import os
import re

import packaging.version


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



def        _lowerCamelCase ( __a,							__a,							__a    ):
		with open(__a,							'''r''',							encoding='''utf-8''',							newline='''\n'''    ) as f:
				SCREAMING_SNAKE_CASE_						 = f.read()
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = REPLACE_PATTERNS[pattern]
		SCREAMING_SNAKE_CASE_						 = replace.replace('''VERSION''',							__a    )
		SCREAMING_SNAKE_CASE_						 = re_pattern.sub(__a,							__a    )
		with open(__a,							'''w''',							encoding='''utf-8''',							newline='''\n'''    ) as f:
				f.write(__a    )



def        _lowerCamelCase ( __a    ):
		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        _lowerCamelCase ( __a,							__a=False    ):
		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(__a,							__a,							__a    )
		if not patch:
				update_version_in_examples(__a    )



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

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

		SCREAMING_SNAKE_CASE_						 = start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt    ):
				if lines[index].startswith('''1.'''    ):
						SCREAMING_SNAKE_CASE_						 = 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        _lowerCamelCase ( ):
		with open(REPLACE_FILES['''init'''],							'''r'''    ) as f:
				SCREAMING_SNAKE_CASE_						 = f.read()
		SCREAMING_SNAKE_CASE_						 = REPLACE_PATTERNS['''init'''][0].search(__a    ).groups()[0]
		return packaging.version.parse(__a    )



def        _lowerCamelCase ( __a=False    ):
		SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_						 = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
		else:
				SCREAMING_SNAKE_CASE_						 = F'{default_version.major}.{default_version.minor + 1}.0'

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

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



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = get_version()
		SCREAMING_SNAKE_CASE_						 = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
		SCREAMING_SNAKE_CASE_						 = current_version.base_version

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

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )