

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
a__												=						{
    '''/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   __UpperCAmelCase					(      __a :     List[str]      )     ->	str:

					"""simple docstring"""

					_a      :       int   =						list(s_dict.keys()      )
					for key in keys:
										_a      :       Union[str, Any]   =						R'''.*/layers_(\d+)'''
										_a      :       List[str]   =						key
										if re.match(__a						,__a      ):
															_a      :       Tuple   =						re.sub(R'''layers_(\d+)'''						,R'''block/\1/layer'''						,__a      )

										_a      :       Union[str, Any]   =						R'''(encoder|decoder)\/'''

										if re.match(__a						,__a      ):
															_a      :       Tuple   =						re.match(__a						,__a      ).groups()
															if groups[0] == "encoder":
																				_a      :       Union[str, Any]   =						re.sub(R'''/mlp/'''						,R'''/1/mlp/'''						,__a      )
																				_a      :       Tuple   =						re.sub(R'''/pre_mlp_layer_norm/'''						,R'''/1/layer_norm/'''						,__a      )

															elif groups[0] == "decoder":
																				_a      :       Dict   =						re.sub(R'''/mlp/'''						,R'''/2/mlp/'''						,__a      )
																				_a      :       Optional[Any]   =						re.sub(R'''/pre_mlp_layer_norm/'''						,R'''/2/layer_norm/'''						,__a      )

        # 2. Convert other classic mappings
										for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
															if old_key in new_key:
																				_a      :       Tuple   =						new_key.replace(__a						,__a      )

										print(F"""{key} -> {new_key}"""      )
										_a      :       List[Any]   =						s_dict.pop(__a      )

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										_a      :       Optional[int]   =						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:
										_a      :       str   =						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:
															_a      :       Optional[Any]   =						s_dict[key].shape[0]
															_a      :       Optional[Any]   =						s_dict[key]
															for idx in range(__a      ):
																				_a      :       List[str]   =						expert_weihts[idx]
																				print(F"""{key} -> {key.replace('expert/'						,'nested fstring'      )}"""      )

															s_dict.pop(__a      )

					return s_dict


a__												=						{
    '''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   __UpperCAmelCase					(      __a :     int						,__a :     Any      )     ->	Union[str, Any]:

					"""simple docstring"""

					import regex as re

					with open(__a						,'''r'''      ) as f:
										_a      :       List[Any]   =						f.read()

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

					_a      :       Dict   =						re.findall(R'''(.*activations) = \(\'(.*)\',\)'''						,__a      )[0]
					_a      :       str   =						str(activation[1]      )

					_a      :       Tuple   =						num_experts
					_a      :       Tuple   =						SwitchTransformersConfig(**__a      )
					return config



def   __UpperCAmelCase					(      __a :     Dict						,__a :     Optional[int]						,__a :     Dict=None						,__a :     int="./"						,__a :     Union[str, Any]=8      )     ->	Dict:

					"""simple docstring"""

					print(F"""Loading flax weights from : {flax_checkpoint_path}"""      )
					_a      :       List[Any]   =						checkpoints.load_tax_checkpoint(__a      )

					if gin_file is not None:
										_a      :       Tuple   =						convert_gin_to_config(__a						,__a      )
					else:
										_a      :       Optional[int]   =						SwitchTransformersConfig.from_pretrained(__a      )

					_a      :       int   =						SwitchTransformersForConditionalGeneration(__a      )

					_a      :       Optional[int]   =						flax_params['''target''']
					_a      :       Union[str, Any]   =						flatten_dict(__a						,sep='''/'''      )
					_a      :       Any   =						rename_keys(__a      )
					_a      :       Any   =						unflatten_dict(__a						,sep='''/'''      )

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

					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					pt_model.save_pretrained(__a      )


if __name__ == "__main__":
			a__												=						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''')
			a__												=						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,
			)





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
