



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:    Optional[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    ):
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	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


_a:    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       ,     A    ):
	# Convert a google style config to the hugging face fromat
	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       __lowerCAmelCase      (						A       ,     A       ,     A=None       ,     A="./"       ,     A=8    ):
	# Initialise PyTorch model

	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__":
				_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:    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,
				)


from sklearn.metrics import mean_squared_error

import datasets


_a:    Any 							=			"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

_a:    List[Any] 							=			"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_a:    List[str] 							=			"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION						)
class 						__UpperCamelCase    (					datasets.Metric						):




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		return datasets.MetricInfo(
		    description=_DESCRIPTION		,	citation=_CITATION		,	inputs_description=_KWARGS_DESCRIPTION		,	features=datasets.Features(self._get_feature_types()							)		,	reference_urls=[
		        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
		    ]		,	)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		if self.config_name == "multilist":
			return {
			    "predictions": datasets.Sequence(datasets.Value("float"							)							),
			    "references": datasets.Sequence(datasets.Value("float"							)							),
			}
		else:
			return {
			    "predictions": datasets.Value("float"							),
			    "references": datasets.Value("float"							),
			}




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						str="uniform_average"		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			mean_squared_error(
		    lowerCAmelCase		,	lowerCAmelCase		,	sample_weight=lowerCAmelCase		,	multioutput=lowerCAmelCase		,	squared=lowerCAmelCase							)

		return {"mse": mse}