





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
UpperCAmelCase_							=				{
    """/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__  (				lowercase				)				-> str:

							"""simple docstring"""



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

														lowercase_	:   str  				=     r"""(encoder|decoder)\/"""

														if re.match(lowercase				,       lowercase				):
																					lowercase_	:   Tuple  				=     re.match(lowercase				,       lowercase				).groups()
																					if groups[0] == "encoder":
																												lowercase_	:   Union[str, Any]  				=     re.sub(r"""/mlp/"""				,       r"""/1/mlp/"""				,       lowercase				)
																												lowercase_	:   Tuple  				=     re.sub(r"""/pre_mlp_layer_norm/"""				,       r"""/1/layer_norm/"""				,       lowercase				)

																					elif groups[0] == "decoder":
																												lowercase_	:   Tuple  				=     re.sub(r"""/mlp/"""				,       r"""/2/mlp/"""				,       lowercase				)
																												lowercase_	:   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:
																												lowercase_	:   Dict  				=     new_key.replace(lowercase				,       lowercase				)

														print(f"""{key} -> {new_key}"""				)
														lowercase_	:   Any  				=     s_dict.pop(lowercase				)

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

																					s_dict.pop(lowercase				)

							return s_dict


UpperCAmelCase_							=				{
    """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__  (				lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



							import regex as re

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

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

							lowercase_	:   str  				=     re.findall(r"""(.*activations) = \(\'(.*)\',\)"""				,       lowercase				)[0]
							lowercase_	:   Tuple  				=     str(activation[1]				)

							lowercase_	:   str  				=     num_experts
							lowercase_	:   Dict  				=     SwitchTransformersConfig(**lowercase				)
							return config





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				,       lowercase="./"				,       lowercase=8				)				-> List[str]:

							"""simple docstring"""



							print(f"""Loading flax weights from : {flax_checkpoint_path}"""				)
							lowercase_	:   Optional[int]  				=     checkpoints.load_tax_checkpoint(lowercase				)

							if gin_file is not None:
														lowercase_	:   Dict  				=     convert_gin_to_config(lowercase				,       lowercase				)
							else:
														lowercase_	:   Any  				=     SwitchTransformersConfig.from_pretrained(lowercase				)

							lowercase_	:   List[str]  				=     SwitchTransformersForConditionalGeneration(lowercase				)

							lowercase_	:   str  				=     flax_params["""target"""]
							lowercase_	:   str  				=     flatten_dict(lowercase				,       sep="""/"""				)
							lowercase_	:   str  				=     rename_keys(lowercase				)
							lowercase_	:   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__":
							UpperCAmelCase_							=				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""")
							UpperCAmelCase_							=				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,
							)






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       )				-> list[float]:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   List[str]  				=     coefficient_matrix.shape
							lowercase_				,		lowercase_	:   Any  				=     constant_matrix.shape

							if rowsa != colsa:
														lowercase_	:   List[Any]  				=     f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if colsa != 1:
														lowercase_	:   Optional[int]  				=     f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if rowsa != rowsa:
														lowercase_	:   Tuple  				=     (
														    """Coefficient and constant matrices dimensions must be nxn and nx1 but """
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(lowercase				)

							if len(lowercase				) != rowsa:
														lowercase_	:   int  				=     (
														    """Number of initial values must be equal to number of rows in coefficient """
														    f"""matrix but received {len(lowercase				)} and {rowsa}"""
														)
														raise ValueError(lowercase				)

							if iterations <= 0:
														raise ValueError("""Iterations must be at least 1"""				)

							lowercase_	:   NDArray[floataa]  				=     np.concatenate(
							    (coefficient_matrix, constant_matrix)				,       axis=1				)

							lowercase_				,		lowercase_	:   Dict  				=     table.shape

							strictly_diagonally_dominant(lowercase				)

							# Iterates the whole matrix for given number of times
							for _ in range(lowercase				):
														lowercase_	:   str  				=     []
														for row in range(lowercase				):
																					lowercase_	:   Dict  				=     0
																					for col in range(lowercase				):
																												if col == row:
																																			lowercase_	:   Optional[Any]  				=     table[row][col]
																												elif col == cols - 1:
																																			lowercase_	:   Tuple  				=     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowercase_	:   List[Any]  				=     (temp + val) / denom
																					new_val.append(lowercase				)
														lowercase_	:   Optional[int]  				=     new_val

							return [float(lowercase				) for i in new_val]





def  __magic_name__  (				lowercase				)				-> bool:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   str  				=     table.shape

							lowercase_	:   str  				=     True

							for i in range(0				,       lowercase				):
														lowercase_	:   Any  				=     0
														for j in range(0				,       cols - 1				):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""				)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
							import doctest

							doctest.testmod()