






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
lowercase  :							List[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      lowerCAmelCase__	(    _a       :			Union[str, Any]    ):
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		snake_case_				:							int						 =       list(s_dict.keys()    )
		for key in keys:
				snake_case_				:							Optional[int]						 =       R".*/layers_(\d+)"
				snake_case_				:							Optional[Any]						 =       key
				if re.match(_a  ,      _a    ):
						snake_case_				:							int						 =       re.sub(R"layers_(\d+)"  ,      R"block/\1/layer"  ,      _a    )

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

				if re.match(_a  ,      _a    ):
						snake_case_				:							Optional[int]						 =       re.match(_a  ,      _a    ).groups()
						if groups[0] == "encoder":
								snake_case_				:							Tuple						 =       re.sub(R"/mlp/"  ,      R"/1/mlp/"  ,      _a    )
								snake_case_				:							Optional[Any]						 =       re.sub(R"/pre_mlp_layer_norm/"  ,      R"/1/layer_norm/"  ,      _a    )

						elif groups[0] == "decoder":
								snake_case_				:							Any						 =       re.sub(R"/mlp/"  ,      R"/2/mlp/"  ,      _a    )
								snake_case_				:							Dict						 =       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:
								snake_case_				:							Any						 =       new_key.replace(_a  ,      _a    )

				print(F'''{key} -> {new_key}'''    )
				snake_case_				:							List[str]						 =       s_dict.pop(_a    )

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

						s_dict.pop(_a    )

		return s_dict


lowercase  :							Union[str, 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      lowerCAmelCase__	(    _a       :			List[Any]  ,      _a       :			Union[str, Any]    ):
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(_a  ,      "r"    ) as f:
				snake_case_				:							Union[str, Any]						 =       f.read()

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

		snake_case_				:							int						 =       re.findall(R"(.*activations) = \(\'(.*)\',\)"  ,      _a    )[0]
		snake_case_				:							List[str]						 =       str(activation[1]    )

		snake_case_				:							int						 =       num_experts
		snake_case_				:							List[Any]						 =       SwitchTransformersConfig(**_a    )
		return config

def      lowerCAmelCase__	(    _a       :			Optional[Any]  ,      _a       :			Optional[Any]  ,      _a       :			Optional[int]=None  ,      _a       :			Tuple="./"  ,      _a       :			int=8    ):
		# Initialise PyTorch model

		print(F'''Loading flax weights from : {flax_checkpoint_path}'''    )
		snake_case_				:							Any						 =       checkpoints.load_tax_checkpoint(_a    )

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

		snake_case_				:							Union[str, Any]						 =       SwitchTransformersForConditionalGeneration(_a    )

		snake_case_				:							Any						 =       flax_params["target"]
		snake_case_				:							List[str]						 =       flatten_dict(_a  ,      sep="/"    )
		snake_case_				:							Optional[Any]						 =       rename_keys(_a    )
		snake_case_				:							Optional[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__":
							lowercase  :							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''')
							lowercase  :							Optional[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,
							)














import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							List[Any]			  =    logging.get_logger(__name__)

lowercase  :							Any			  =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[int]						=					'sew-d'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=("p2c", "c2p")		,				_SCREAMING_SNAKE_CASE="layer_norm"		,				_SCREAMING_SNAKE_CASE="gelu_python"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-7		,				_SCREAMING_SNAKE_CASE=1e-5		,				_SCREAMING_SNAKE_CASE="group"		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.05		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE="mean"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[Any]:
								super().__init__(**_SCREAMING_SNAKE_CASE		,				pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       hidden_size
								snake_case_				:							Any						 =       feat_extract_norm
								snake_case_				:							Union[str, Any]						 =       feat_extract_activation
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       conv_bias
								snake_case_				:							Union[str, Any]						 =       num_conv_pos_embeddings
								snake_case_				:							Tuple						 =       num_conv_pos_embedding_groups
								snake_case_				:							Optional[int]						 =       len(self.conv_dim				)
								snake_case_				:							str						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       intermediate_size
								snake_case_				:							List[Any]						 =       squeeze_factor
								snake_case_				:							Tuple						 =       max_position_embeddings
								snake_case_				:							Optional[int]						 =       position_buckets
								snake_case_				:							Union[str, Any]						 =       share_att_key
								snake_case_				:							Optional[int]						 =       relative_attention
								snake_case_				:							List[str]						 =       norm_rel_ebd
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       hidden_act
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							Dict						 =       hidden_dropout
								snake_case_				:							int						 =       attention_dropout
								snake_case_				:							Any						 =       activation_dropout
								snake_case_				:							Optional[Any]						 =       feat_proj_dropout
								snake_case_				:							Tuple						 =       final_dropout
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							Tuple						 =       feature_layer_norm_eps
								snake_case_				:							Any						 =       initializer_range
								snake_case_				:							Optional[int]						 =       vocab_size

								if (
								    (len(self.conv_stride				) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
								    or (len(self.conv_dim				) != self.num_feat_extract_layers)
								):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f'''but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'''
										    f'''= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								snake_case_				:							List[str]						 =       apply_spec_augment
								snake_case_				:							List[Any]						 =       mask_time_prob
								snake_case_				:							str						 =       mask_time_length
								snake_case_				:							Optional[Any]						 =       mask_time_min_masks
								snake_case_				:							Optional[Any]						 =       mask_feature_prob
								snake_case_				:							str						 =       mask_feature_length
								snake_case_				:							Optional[int]						 =       mask_feature_min_masks

								# ctc loss
								snake_case_				:							Any						 =       ctc_loss_reduction
								snake_case_				:							Tuple						 =       ctc_zero_infinity

								# sequence classification
								snake_case_				:							int						 =       use_weighted_layer_sum
								snake_case_				:							List[Any]						 =       classifier_proj_size






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								return functools.reduce(operator.mul		,				self.conv_stride		,				1				)
