






'''simple docstring'''


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_				: Dict    	=							{
    '''/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 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = list(s_dict.keys()					)
						for key in keys:
												UpperCamelCase__         = r""".*/layers_(\d+)"""
												UpperCamelCase__         = key
												if re.match(_A						,      _A					):
																		UpperCamelCase__         = re.sub(r"""layers_(\d+)"""						,      r"""block/\1/layer"""						,      _A					)

												UpperCamelCase__         = r"""(encoder|decoder)\/"""

												if re.match(_A						,      _A					):
																		UpperCamelCase__         = re.match(_A						,      _A					).groups()
																		if groups[0] == "encoder":
																								UpperCamelCase__         = re.sub(r"""/mlp/"""						,      r"""/1/mlp/"""						,      _A					)
																								UpperCamelCase__         = re.sub(r"""/pre_mlp_layer_norm/"""						,      r"""/1/layer_norm/"""						,      _A					)

																		elif groups[0] == "decoder":
																								UpperCamelCase__         = re.sub(r"""/mlp/"""						,      r"""/2/mlp/"""						,      _A					)
																								UpperCamelCase__         = 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:
																								UpperCamelCase__         = new_key.replace(_A						,      _A					)

												print(f"{key} -> {new_key}"					)
												UpperCamelCase__         = s_dict.pop(_A					)

						if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												UpperCamelCase__         = 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:
												UpperCamelCase__         = 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:
																		UpperCamelCase__         = s_dict[key].shape[0]
																		UpperCamelCase__         = s_dict[key]
																		for idx in range(_A					):
																								UpperCamelCase__         = expert_weihts[idx]
																								print(f"{key} -> {key.replace('expert/'						,      'nested fstring'					)}"					)

																		s_dict.pop(_A					)

						return s_dict


lowerCamelCase_				: List[str]    	=							{
    '''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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						import regex as re

						with open(_A						,      """r"""					) as f:
												UpperCamelCase__         = f.read()

						UpperCamelCase__         = re.findall(r"""(.*) = ([0-9.]*)"""						,      _A					)
						UpperCamelCase__         = {}
						for param, value in regex_match:
												if param in GIN_TO_CONFIG_MAPPING and value != "":
																		UpperCamelCase__         = float(_A					) if """.""" in value else int(_A					)

						UpperCamelCase__         = re.findall(r"""(.*activations) = \(\'(.*)\',\)"""						,      _A					)[0]
						UpperCamelCase__         = str(activation[1]					)

						UpperCamelCase__         = num_experts
						UpperCamelCase__         = SwitchTransformersConfig(**_A					)
						return config







def 	__magic_name__( _A						,      _A						,      _A=None						,      _A="./"						,      _A=8					):
						'''simple docstring'''





						print(f"Loading flax weights from : {flax_checkpoint_path}"					)
						UpperCamelCase__         = checkpoints.load_tax_checkpoint(_A					)

						if gin_file is not None:
												UpperCamelCase__         = convert_gin_to_config(_A						,      _A					)
						else:
												UpperCamelCase__         = SwitchTransformersConfig.from_pretrained(_A					)

						UpperCamelCase__         = SwitchTransformersForConditionalGeneration(_A					)

						UpperCamelCase__         = flax_params["""target"""]
						UpperCamelCase__         = flatten_dict(_A						,      sep="""/"""					)
						UpperCamelCase__         = rename_keys(_A					)
						UpperCamelCase__         = 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__":
					lowerCamelCase_				: Optional[int]    	=							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_				: str    	=							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,
					)








'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE	)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							str					     =					field(default="audio-classification"       ,metadata={"include_in_asdict_even_if_is_default": True}	)
			__a							:							ClassVar[Features]					     =					Features({"audio": Audio()}	)
			__a							:							ClassVar[Features]					     =					Features({"labels": ClassLabel}	)
			__a							:							str					     =					"audio"
			__a							:							str					     =					"labels"






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any]			)							->    Any:

									'''simple docstring'''







									if self.label_column not in features:
															raise ValueError(f"Column {self.label_column} is not present in features."			)
									if not isinstance(features[self.label_column] ,      lowercase			):
															raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
									UpperCamelCase__         = copy.deepcopy(self			)
									UpperCamelCase__         = self.label_schema.copy()
									UpperCamelCase__         = features[self.label_column]
									UpperCamelCase__         = label_schema
									return task_template







			@property
			def 				A      (   self							:						int			)							->    Dict[str, str]:

									'''simple docstring'''







									return {
									    self.audio_column: "audio",
									    self.label_column: "labels",
									}
