




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      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Tuple					):





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







def      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    str					):





					for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE__					):
										# 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(SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__					)				,				SCREAMING_SNAKE_CASE__				,				pattern="examples"					)







def      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=False					):





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







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

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

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

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







def      UpperCAmelCase_				(						):





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







def      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    str=False					):
					__lowerCAmelCase           =	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:
										__lowerCAmelCase           =	default_version.base_version
					elif patch:
										__lowerCAmelCase           =	F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
					else:
										__lowerCAmelCase           =	F"""{default_version.major}.{default_version.minor + 1}.0"""

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

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







def      UpperCAmelCase_				(						):
					__lowerCAmelCase           =	get_version()
					__lowerCAmelCase           =	F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
					__lowerCAmelCase           =	current_version.base_version

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

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


#    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
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

					for key, value in state_dict.items():
										if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
															continue

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				atol=1E-3					)

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




