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,
		)

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [self.constructed_objects[key_node] for key_node, _ in node.value]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(_A    ) if isinstance(_A,_A    ) else key for key in keys]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Counter(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
									raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







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







		SCREAMING_SNAKE_CASE_  :		str				      =    list(readme_content.splitlines()	)
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    full_content[1:].index("---"	) + 1
				SCREAMING_SNAKE_CASE_  :		int				      =    "\n".join(full_content[1:sep_idx]	)
				return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

		return None, "\n".join(lowerCAmelCase	)






class 		a__						(					A__					):
					# class attributes
					A	       =							{'train_eval_index'}  # train-eval-index in the YAML metadata

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






							with open(_A,encoding="utf-8"    ) as readme_file:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    _split_yaml_from_readme(readme_file.read()    )
							if yaml_string is not None:
									return cls.from_yaml_string(_A    )
							else:
									return cls()

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






							if path.exists():
									with open(_A,encoding="utf-8"    ) as readme_file:
											SCREAMING_SNAKE_CASE_  :		int				      =    readme_file.read()
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		int				      =    self._to_readme(_A    )
							with open(_A,"w",encoding="utf-8"    ) as readme_file:
									readme_file.write(_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

							# Convert the YAML keys to DatasetMetadata fields
							SCREAMING_SNAKE_CASE_  :		Any				      =    {
							    (key.replace("-","_"    ) if key.replace("-","_"    ) in cls._FIELDS_WITH_DASHES else key): value
							    for key, value in metadata_dict.items()
							}
							return cls(**_A    )






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							return yaml.safe_dump(
							    {
							        (key.replace("_","-"    ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    },sort_keys=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	List[Any]       =  {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		__lowerCamelCase					:	List[Any]       =  ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		__lowerCamelCase					:	Dict       =  ap.parse_args()

		__lowerCamelCase					:	List[Any]       =  Path(args.readme_filepath)
		__lowerCamelCase					:	Optional[int]       =  DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)
