import argparse
import os
import re

import packaging.version


__lowerCamelCase					:	str       =  '''examples/'''
__lowerCamelCase					:	Dict       =  {
    '''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					:	Tuple       =  {
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
__lowerCamelCase					:	str       =  '''README.md'''




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







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




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







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




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[int]=False	):
		"""simple docstring"""







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




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    "🤗 Transformers currently provides the following architectures"
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    "1. Want to contribute a new model?"
		with open(lowerCAmelCase   , "r"   , encoding="utf-8"   , newline="\n"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.readlines()

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

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

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




def       _snake_case	(   ):
		"""simple docstring"""







		with open(REPLACE_FILES["init"]   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Any				      =    f.read()
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    REPLACE_PATTERNS["init"][0].search(lowerCAmelCase	).groups()[0]
		return packaging.version.parse(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   int=False	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    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_  :		str				      =    default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
		else:
				SCREAMING_SNAKE_CASE_  :		int				      =    f'{default_version.major}.{default_version.minor + 1}.0'

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

		print(f'Updating version to {version}.'	)
		global_version_update(lowerCAmelCase   , patch=lowerCAmelCase	)




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    get_version()
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    f'{current_version.major}.{current_version.minor + 1}.0.dev0'
		SCREAMING_SNAKE_CASE_  :		int				      =    current_version.base_version

		# Check with the user we got that right.
		SCREAMING_SNAKE_CASE_  :		Dict				      =    input(f'Which version are we developing now? [{dev_version}]'	)
		if len(lowerCAmelCase	) == 0:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    dev_version

		print(f'Updating version to {version}.'	)
		global_version_update(lowerCAmelCase	)


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


if __name__ == "__main__":
		__lowerCamelCase					:	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.''')
		__lowerCamelCase					:	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()

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCamelCase					:	Any       =  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    list(s_dict.keys()	)
		for key in keys:
				SCREAMING_SNAKE_CASE_  :		int				      =    R".*/layers_(\d+)"
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    key
				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"layers_(\d+)"   , R"block/\1/layer"   , lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    R"(encoder|decoder)\/"

				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		str				      =    re.match(lowerCAmelCase   , lowerCAmelCase	).groups()
						if groups[0] == "encoder":
								SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"/mlp/"   , R"/1/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/1/layer_norm/"   , lowerCAmelCase	)

						elif groups[0] == "decoder":
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.sub(R"/mlp/"   , R"/2/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/2/layer_norm/"   , lowerCAmelCase	)

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    new_key.replace(lowerCAmelCase   , lowerCAmelCase	)

				print(f'{key} -> {new_key}'	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict.pop(lowerCAmelCase	)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_SNAKE_CASE_  :		str				      =    s_dict[
				    "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
				].T
		if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    s_dict[
				    "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
				].T

		# 3. Take extra care of the EXPERTS layer
		for key in list(s_dict.keys()	):
				if "expert" in key:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    s_dict[key].shape[0]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict[key]
						for idx in range(lowerCAmelCase	):
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    expert_weihts[idx]
								print(f'{key} -> {key.replace("expert/"   , "nested fstring"	)}'	)

						s_dict.pop(lowerCAmelCase	)

		return s_dict


__lowerCamelCase					:	List[Any]       =  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}




def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		import regex as re

		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.read()

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.findall(R"(.*) = ([0-9.]*)"   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    {}
		for param, value in regex_match:
				if param in GIN_TO_CONFIG_MAPPING and value != "":
						SCREAMING_SNAKE_CASE_  :		int				      =    float(lowerCAmelCase	) if "." in value else int(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    re.findall(R"(.*activations) = \(\'(.*)\',\)"   , lowerCAmelCase	)[0]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    str(activation[1]	)

		SCREAMING_SNAKE_CASE_  :		str				      =    num_experts
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SwitchTransformersConfig(**lowerCAmelCase	)
		return config




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Optional[Any]="./"   , lowerCAmelCase							:   Dict=8	):
		"""simple docstring"""







		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		SCREAMING_SNAKE_CASE_  :		int				      =    checkpoints.load_tax_checkpoint(lowerCAmelCase	)

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    convert_gin_to_config(lowerCAmelCase   , lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		str				      =    SwitchTransformersForConditionalGeneration(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    flax_params["target"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    flatten_dict(lowerCAmelCase   , sep="/"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    rename_keys(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    unflatten_dict(lowerCAmelCase   , sep="/"	)

		# Load the flax params in the PT model
		load_flax_weights_in_pytorch_model(lowerCAmelCase   , lowerCAmelCase	)

		print(f'Save PyTorch model to {pytorch_dump_path}'	)
		pt_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[str]       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--switch_t5x_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
		        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
		    ),
		)
		parser.add_argument(
		    '''--gin_file''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
		)
		parser.add_argument(
		    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
		)
		parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
		__lowerCamelCase					:	Any       =  parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)
