



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

														UpperCAmelCase     :		List[Any]							     =		R"""(encoder|decoder)\/"""

														if re.match(_lowercase	,				_lowercase		):
																					UpperCAmelCase     :		List[str]							     =		re.match(_lowercase	,				_lowercase		).groups()
																					if groups[0] == "encoder":
																												UpperCAmelCase     :		Dict							     =		re.sub(R"""/mlp/"""	,				R"""/1/mlp/"""	,				_lowercase		)
																												UpperCAmelCase     :		Union[str, Any]							     =		re.sub(R"""/pre_mlp_layer_norm/"""	,				R"""/1/layer_norm/"""	,				_lowercase		)

																					elif groups[0] == "decoder":
																												UpperCAmelCase     :		List[Any]							     =		re.sub(R"""/mlp/"""	,				R"""/2/mlp/"""	,				_lowercase		)
																												UpperCAmelCase     :		Dict							     =		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:
																												UpperCAmelCase     :		str							     =		new_key.replace(_lowercase	,				_lowercase		)

														print(F'''{key} -> {new_key}'''		)
														UpperCAmelCase     :		str							     =		s_dict.pop(_lowercase		)

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

																					s_dict.pop(_lowercase		)

							return s_dict


a   :			Optional[int]					  =    {
    """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  (					_lowercase	,				_lowercase		)      ->			List[Any]:
							# Convert a google style config to the hugging face fromat
							import regex as re

							with open(_lowercase	,				"""r"""		) as f:
														UpperCAmelCase     :		Any							     =		f.read()

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

							UpperCAmelCase     :		List[str]							     =		re.findall(R"""(.*activations) = \(\'(.*)\',\)"""	,				_lowercase		)[0]
							UpperCAmelCase     :		Optional[Any]							     =		str(activation[1]		)

							UpperCAmelCase     :		List[str]							     =		num_experts
							UpperCAmelCase     :		List[str]							     =		SwitchTransformersConfig(**_lowercase		)
							return config



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=None	,				_lowercase="./"	,				_lowercase=8		)      ->			Optional[Any]:
							# Initialise PyTorch model

							print(F'''Loading flax weights from : {flax_checkpoint_path}'''		)
							UpperCAmelCase     :		List[Any]							     =		checkpoints.load_tax_checkpoint(_lowercase		)

							if gin_file is not None:
														UpperCAmelCase     :		List[str]							     =		convert_gin_to_config(_lowercase	,				_lowercase		)
							else:
														UpperCAmelCase     :		Any							     =		SwitchTransformersConfig.from_pretrained(_lowercase		)

							UpperCAmelCase     :		Any							     =		SwitchTransformersForConditionalGeneration(_lowercase		)

							UpperCAmelCase     :		Tuple							     =		flax_params["""target"""]
							UpperCAmelCase     :		List[str]							     =		flatten_dict(_lowercase	,				sep="""/"""		)
							UpperCAmelCase     :		List[Any]							     =		rename_keys(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		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   :			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""")
							a   :			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,
							)






'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		gather(_lowercase		)
							assert gathered_tensor.tolist() == list(range(1	,				state.num_processes**2 + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Any							     =		[state.process_index]
							UpperCAmelCase     :		Union[str, Any]							     =		gather_object(_lowercase		)
							assert len(_lowercase		) == state.num_processes, F'''{gathered_obj}, {len(_lowercase		)} != {state.num_processes}'''
							assert gathered_obj == list(range(state.num_processes		)		), F'''{gathered_obj} != {list(range(state.num_processes		)		)}'''



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Optional[int]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		broadcast(_lowercase		)
							assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
							assert broadcasted_tensor.tolist() == list(range(1	,				state.num_processes + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# We need to pad the tensor with one more element if we are the main process
							# to ensure that we can pad
							if state.is_main_process:
														UpperCAmelCase     :		Optional[Any]							     =		torch.arange(state.num_processes + 1		).to(state.device		)
							else:
														UpperCAmelCase     :		Tuple							     =		torch.arange(state.num_processes		).to(state.device		)
							UpperCAmelCase     :		Optional[Any]							     =		pad_across_processes(_lowercase		)
							assert padded_tensor.shape == torch.Size([state.num_processes + 1]		)
							if not state.is_main_process:
														assert padded_tensor.tolist() == list(range(0	,				state.num_processes		)		) + [0]



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Optional[Any]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		reduce(_lowercase	,				"""sum"""		)
							UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([4.0, 6]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Tuple							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		reduce(_lowercase	,				"""mean"""		)
							UpperCAmelCase     :		str							     =		torch.tensor([2.0, 3]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							# For xla_spawn (TPUs)
							main()



def       __lowerCamelCase  (					)      ->			int:
							UpperCAmelCase     :		List[Any]							     =		PartialState()
							state.print(F'''State: {state}'''		)
							state.print("""testing gather"""		)
							test_gather(_lowercase		)
							state.print("""testing gather_object"""		)
							test_gather_object(_lowercase		)
							state.print("""testing broadcast"""		)
							test_broadcast(_lowercase		)
							state.print("""testing pad_across_processes"""		)
							test_pad_across_processes(_lowercase		)
							state.print("""testing reduce_sum"""		)
							test_reduce_sum(_lowercase		)
							state.print("""testing reduce_mean"""		)
							test_reduce_mean(_lowercase		)


if __name__ == "__main__":
							main()

