



"""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
__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    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			list(s_dict.keys()	)
						for key in keys:
												snake_case_ :Dict						=			r""".*/layers_(\d+)"""
												snake_case_ :Dict						=			key
												if re.match(_lowercase,					_lowercase	):
																		snake_case_ :Dict						=			re.sub(r"""layers_(\d+)""",					r"""block/\1/layer""",					_lowercase	)

												snake_case_ :List[Any]						=			r"""(encoder|decoder)\/"""

												if re.match(_lowercase,					_lowercase	):
																		snake_case_ :Any						=			re.match(_lowercase,					_lowercase	).groups()
																		if groups[0] == "encoder":
																								snake_case_ :str						=			re.sub(r"""/mlp/""",					r"""/1/mlp/""",					_lowercase	)
																								snake_case_ :Optional[Any]						=			re.sub(r"""/pre_mlp_layer_norm/""",					r"""/1/layer_norm/""",					_lowercase	)

																		elif groups[0] == "decoder":
																								snake_case_ :int						=			re.sub(r"""/mlp/""",					r"""/2/mlp/""",					_lowercase	)
																								snake_case_ :Optional[Any]						=			re.sub(r"""/pre_mlp_layer_norm/""",					r"""/2/layer_norm/""",					_lowercase	)

        # 2. Convert other classic mappings
												for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																		if old_key in new_key:
																								snake_case_ :Union[str, Any]						=			new_key.replace(_lowercase,					_lowercase	)

												print(f"""{key} -> {new_key}"""	)
												snake_case_ :List[Any]						=			s_dict.pop(_lowercase	)

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

																		s_dict.pop(_lowercase	)

						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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						import regex as re

						with open(_lowercase,					"""r"""	) as f:
												snake_case_ :Tuple						=			f.read()

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

						snake_case_ :Dict						=			re.findall(r"""(.*activations) = \(\'(.*)\',\)""",					_lowercase	)[0]
						snake_case_ :Optional[int]						=			str(activation[1]	)

						snake_case_ :Dict						=			num_experts
						snake_case_ :int						=			SwitchTransformersConfig(**_lowercase	)
						return config
def    A_  (			_lowercase,					_lowercase,					_lowercase=None,					_lowercase="./",					_lowercase=8	):







						'''simple docstring'''


						print(f"""Loading flax weights from : {flax_checkpoint_path}"""	)
						snake_case_ :List[Any]						=			checkpoints.load_tax_checkpoint(_lowercase	)

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

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

						snake_case_ :Tuple						=			flax_params["""target"""]
						snake_case_ :Any						=			flatten_dict(_lowercase,					sep="""/"""	)
						snake_case_ :int						=			rename_keys(_lowercase	)
						snake_case_ :List[Any]						=			unflatten_dict(_lowercase,					sep="""/"""	)

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

						print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
						pt_model.save_pretrained(_lowercase	)


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,
						)





"""simple docstring"""
import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							List[str]						,     snake_case:							Optional[Any]=13						,     snake_case:							List[str]=7						,     snake_case:							Dict=True						,     snake_case:							List[str]=True						,     snake_case:							Optional[int]=True						,     snake_case:							Any=True						,     snake_case:							Optional[Any]=99						,     snake_case:							Tuple=32						,     snake_case:							Tuple=5						,     snake_case:							Dict=4						,     snake_case:							Optional[Any]=37						,     snake_case:							Union[str, Any]="gelu"						,     snake_case:							Tuple=0.1						,     snake_case:							List[Any]=0.1						,     snake_case:							List[str]=512						,     snake_case:							Optional[int]=16						,     snake_case:							int=2						,     snake_case:							List[Any]=0.0_2						,     snake_case:							Union[str, Any]=4						,     )   ->	List[str]:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :Any						=			seq_length
											snake_case_ :List[str]						=			is_training
											snake_case_ :Optional[Any]						=			use_attention_mask
											snake_case_ :Dict						=			use_token_type_ids
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :str						=			vocab_size
											snake_case_ :int						=			hidden_size
											snake_case_ :List[str]						=			num_hidden_layers
											snake_case_ :Dict						=			num_attention_heads
											snake_case_ :Any						=			intermediate_size
											snake_case_ :Tuple						=			hidden_act
											snake_case_ :int						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Any						=			max_position_embeddings
											snake_case_ :Union[str, Any]						=			type_vocab_size
											snake_case_ :Optional[int]						=			type_sequence_label_size
											snake_case_ :Union[str, Any]						=			initializer_range
											snake_case_ :Tuple						=			num_choices






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	str:
											snake_case_ :Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

											snake_case_ :Union[str, Any]						=			None
											if self.use_attention_mask:
																	snake_case_ :str						=			random_attention_mask([self.batch_size, self.seq_length]						)

											snake_case_ :Any						=			None
											if self.use_token_type_ids:
																	snake_case_ :List[str]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

											snake_case_ :int						=			BertConfig(
											    vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=snake_case						,     initializer_range=self.initializer_range						,     )

											return config, input_ids, token_type_ids, attention_mask






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											snake_case_ :str						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Optional[int]						=			config_and_inputs
											snake_case_ :Union[str, Any]						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Any:
											snake_case_ :int						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Dict						=			config_and_inputs

											snake_case_ :Union[str, Any]						=			True
											snake_case_ :Optional[int]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											snake_case_ :Tuple						=			ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2						)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)

@require_flax
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     True

					_A		:			Dict						=     (
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			FlaxBertModelTester(self						)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											snake_case_ :Dict						=			FlaxBertModel.from_pretrained("""bert-base-cased"""						)
											snake_case_ :Dict						=			model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)
