



import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


lowercase_										=  (
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


lowercase_										=  logging.get_logger(__name__)  # pylint: disable=invalid-name



def __lowerCAmelCase			(  ):
			lowercase__       	=					"https://pypi.org/pypi/diffusers/json"
			lowercase__       	=					json.loads(request.urlopen(SCREAMING_SNAKE_CASE_	).read()	)["releases"].keys()
			return sorted(SCREAMING_SNAKE_CASE_    ,	key=lambda SCREAMING_SNAKE_CASE_	: version.Version(SCREAMING_SNAKE_CASE_	)	)



def __lowerCAmelCase			(  ):
			# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
			if HF_MODULES_CACHE in sys.path:
						return

			sys.path.append(SCREAMING_SNAKE_CASE_	)
			os.makedirs(SCREAMING_SNAKE_CASE_    ,	exist_ok=SCREAMING_SNAKE_CASE_	)
			lowercase__       	=					Path(SCREAMING_SNAKE_CASE_	) / "__init__.py"
			if not init_path.exists():
						init_path.touch()



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			init_hf_modules()
			lowercase__       	=					Path(SCREAMING_SNAKE_CASE_	) / name
			# If the parent module does not exist yet, recursively create it.
			if not dynamic_module_path.parent.exists():
						create_dynamic_module(dynamic_module_path.parent	)
			os.makedirs(SCREAMING_SNAKE_CASE_    ,	exist_ok=SCREAMING_SNAKE_CASE_	)
			lowercase__       	=					dynamic_module_path / "__init__.py"
			if not init_path.exists():
						init_path.touch()



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			with open(SCREAMING_SNAKE_CASE_    ,	"r"    ,	encoding="utf-8"	) as f:
						lowercase__       	=					f.read()

			# Imports of the form `import .xxx`
			lowercase__       	=					re.findall("^\s*import\s+\.(\S+)\s*$"    ,	SCREAMING_SNAKE_CASE_    ,	flags=re.MULTILINE	)
			# Imports of the form `from .xxx import yyy`
			relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import"    ,	SCREAMING_SNAKE_CASE_    ,	flags=re.MULTILINE	)
			# Unique-ify
			return list(set(SCREAMING_SNAKE_CASE_	)	)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					False
			lowercase__       	=					[module_file]
			lowercase__       	=					[]

			# Let's recurse through all relative imports
			while not no_change:
						lowercase__       	=					[]
						for f in files_to_check:
									new_imports.extend(get_relative_imports(SCREAMING_SNAKE_CASE_	)	)

						lowercase__       	=					Path(SCREAMING_SNAKE_CASE_	).parent
						lowercase__       	=					[str(module_path / m	) for m in new_imports]
						lowercase__       	=					[f for f in new_import_files if f not in all_relative_imports]
						lowercase__       	=					[f'''{f}.py''' for f in new_import_files]

						lowercase__       	=					len(SCREAMING_SNAKE_CASE_	) == 0
						all_relative_imports.extend(SCREAMING_SNAKE_CASE_	)

			return all_relative_imports



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			with open(SCREAMING_SNAKE_CASE_    ,	"r"    ,	encoding="utf-8"	) as f:
						lowercase__       	=					f.read()

			# Imports of the form `import xxx`
			lowercase__       	=					re.findall("^\s*import\s+(\S+)\s*$"    ,	SCREAMING_SNAKE_CASE_    ,	flags=re.MULTILINE	)
			# Imports of the form `from xxx import yyy`
			imports += re.findall("^\s*from\s+(\S+)\s+import"    ,	SCREAMING_SNAKE_CASE_    ,	flags=re.MULTILINE	)
			# Only keep the top-level module
			lowercase__       	=					[imp.split("."	)[0] for imp in imports if not imp.startswith("."	)]

			# Unique-ify and test we got them all
			lowercase__       	=					list(set(SCREAMING_SNAKE_CASE_	)	)
			lowercase__       	=					[]
			for imp in imports:
						try:
									importlib.import_module(SCREAMING_SNAKE_CASE_	)
						except ImportError:
									missing_packages.append(SCREAMING_SNAKE_CASE_	)

			if len(SCREAMING_SNAKE_CASE_	) > 0:
						raise ImportError(
						    "This modeling file requires the following packages that were not found in your environment: "
						    f'''{", ".join(SCREAMING_SNAKE_CASE_	)}. Run `pip install {" ".join(SCREAMING_SNAKE_CASE_	)}`'''	)

			return get_relative_imports(SCREAMING_SNAKE_CASE_	)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					module_path.replace(os.path.sep    ,	"."	)
			lowercase__       	=					importlib.import_module(SCREAMING_SNAKE_CASE_	)

			if class_name is None:
						return find_pipeline_class(SCREAMING_SNAKE_CASE_	)
			return getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			from ..pipelines import DiffusionPipeline

			lowercase__       	=					dict(inspect.getmembers(SCREAMING_SNAKE_CASE_    ,	inspect.isclass	)	)

			lowercase__       	=					None
			for cls_name, cls in cls_members.items():
						if (
						    cls_name != DiffusionPipeline.__name__
						    and issubclass(cls    ,	SCREAMING_SNAKE_CASE_	)
						    and cls.__module__.split("."	)[0] != "diffusers"
						):
									if pipeline_class is not None:
												raise ValueError(
												    f'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
												    f''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
												    f''' {loaded_module}.'''	)
									lowercase__       	=					cls

			return pipeline_class



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = False    ,	SCREAMING_SNAKE_CASE_ = False    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = False    ,	):
			lowercase__       	=					str(SCREAMING_SNAKE_CASE_	)

			lowercase__       	=					os.path.join(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	)

			if os.path.isfile(SCREAMING_SNAKE_CASE_	):
						lowercase__       	=					module_file_or_url
						lowercase__       	=					"local"
			elif pretrained_model_name_or_path.count("/"	) == 0:
						lowercase__       	=					get_diffusers_versions()
						# cut ".dev0"
						lowercase__       	=					"v" + ".".join(__version__.split("."	)[:3]	)

						# retrieve github version that matches
						if revision is None:
									lowercase__       	=					latest_version if latest_version[1:] in available_versions else "main"
									logger.info(f'''Defaulting to latest_version: {revision}.'''	)
						elif revision in available_versions:
									lowercase__       	=					f'''v{revision}'''
						elif revision == "main":
									lowercase__       	=					revision
						else:
									raise ValueError(
									    f'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
									    f''' {", ".join(available_versions + ["main"]	)}.'''	)

						# community pipeline on GitHub
						lowercase__       	=					COMMUNITY_PIPELINES_URL.format(revision=SCREAMING_SNAKE_CASE_    ,	pipeline=SCREAMING_SNAKE_CASE_	)
						try:
									lowercase__       	=					cached_download(
									    SCREAMING_SNAKE_CASE_    ,	cache_dir=SCREAMING_SNAKE_CASE_    ,	force_download=SCREAMING_SNAKE_CASE_    ,	proxies=SCREAMING_SNAKE_CASE_    ,	resume_download=SCREAMING_SNAKE_CASE_    ,	local_files_only=SCREAMING_SNAKE_CASE_    ,	use_auth_token=SCREAMING_SNAKE_CASE_    ,	)
									lowercase__       	=					"git"
									lowercase__       	=					pretrained_model_name_or_path + ".py"
						except EnvironmentError:
									logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''	)
									raise
			else:
						try:
									# Load from URL or cache if already cached
									lowercase__       	=					hf_hub_download(
									    SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	cache_dir=SCREAMING_SNAKE_CASE_    ,	force_download=SCREAMING_SNAKE_CASE_    ,	proxies=SCREAMING_SNAKE_CASE_    ,	resume_download=SCREAMING_SNAKE_CASE_    ,	local_files_only=SCREAMING_SNAKE_CASE_    ,	use_auth_token=SCREAMING_SNAKE_CASE_    ,	)
									lowercase__       	=					os.path.join("local"    ,	"--".join(pretrained_model_name_or_path.split("/"	)	)	)
						except EnvironmentError:
									logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''	)
									raise

    # Check we have all the requirements in our environment
			lowercase__       	=					check_imports(SCREAMING_SNAKE_CASE_	)

			# Now we move the module inside our cached dynamic modules.
			lowercase__       	=					DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
			create_dynamic_module(SCREAMING_SNAKE_CASE_	)
			lowercase__       	=					Path(SCREAMING_SNAKE_CASE_	) / full_submodule
			if submodule == "local" or submodule == "git":
						# We always copy local files (we could hash the file to see if there was a change, and give them the name of
						# that hash, to only copy when there is a modification but it seems overkill for now).
						# The only reason we do the copy is to avoid putting too many folders in sys.path.
						shutil.copy(SCREAMING_SNAKE_CASE_    ,	submodule_path / module_file	)
						for module_needed in modules_needed:
									lowercase__       	=					f'''{module_needed}.py'''
									shutil.copy(os.path.join(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	)    ,	submodule_path / module_needed	)
			else:
						# Get the commit hash
						# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
						if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
									lowercase__       	=					use_auth_token
						elif use_auth_token is True:
									lowercase__       	=					HfFolder.get_token()
						else:
									lowercase__       	=					None

						lowercase__       	=					model_info(SCREAMING_SNAKE_CASE_    ,	revision=SCREAMING_SNAKE_CASE_    ,	token=SCREAMING_SNAKE_CASE_	).sha

						# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
						# benefit of versioning.
						lowercase__       	=					submodule_path / commit_hash
						lowercase__       	=					full_submodule + os.path.sep + commit_hash
						create_dynamic_module(SCREAMING_SNAKE_CASE_	)

						if not (submodule_path / module_file).exists():
									shutil.copy(SCREAMING_SNAKE_CASE_    ,	submodule_path / module_file	)
						# Make sure we also have every file with relative
						for module_needed in modules_needed:
									if not (submodule_path / module_needed).exists():
												get_cached_module_file(
												    SCREAMING_SNAKE_CASE_    ,	f'''{module_needed}.py'''    ,	cache_dir=SCREAMING_SNAKE_CASE_    ,	force_download=SCREAMING_SNAKE_CASE_    ,	resume_download=SCREAMING_SNAKE_CASE_    ,	proxies=SCREAMING_SNAKE_CASE_    ,	use_auth_token=SCREAMING_SNAKE_CASE_    ,	revision=SCREAMING_SNAKE_CASE_    ,	local_files_only=SCREAMING_SNAKE_CASE_    ,	)
			return os.path.join(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	)




def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = False    ,	SCREAMING_SNAKE_CASE_ = False    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = False    ,	**SCREAMING_SNAKE_CASE_    ,	):
			lowercase__       	=					get_cached_module_file(
			    SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	cache_dir=SCREAMING_SNAKE_CASE_    ,	force_download=SCREAMING_SNAKE_CASE_    ,	resume_download=SCREAMING_SNAKE_CASE_    ,	proxies=SCREAMING_SNAKE_CASE_    ,	use_auth_token=SCREAMING_SNAKE_CASE_    ,	revision=SCREAMING_SNAKE_CASE_    ,	local_files_only=SCREAMING_SNAKE_CASE_    ,	)
			return get_class_in_module(SCREAMING_SNAKE_CASE_    ,	final_module.replace(".py"    ,	""	)	)










import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)