


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase="attention"     ):
							__snake_case		:      str										=       np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]     )
							__snake_case		:      int										=       k_tmp.reshape(k_tmp.shape[0]							,     k_tmp.shape[1] * k_tmp.shape[2]     )
							__snake_case		:      Optional[int]										=       np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]     )
							__snake_case		:      str										=       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,     o_tmp.shape[2]     )
							__snake_case		:      Union[str, Any]										=       np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]     )
							__snake_case		:      int										=       q_tmp.reshape(q_tmp.shape[0]							,     q_tmp.shape[1] * q_tmp.shape[2]     )
							__snake_case		:      Tuple										=       np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]     )
							__snake_case		:      List[Any]										=       v_tmp.reshape(v_tmp.shape[0]							,     v_tmp.shape[1] * v_tmp.shape[2]     )
							return k, o, q, v







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=False     ):
							if split_mlp_wi:
														__snake_case		:      str										=       params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														__snake_case		:      Optional[Any]										=       params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														__snake_case		:      str										=       (wi_a, wi_a)
							else:
														__snake_case		:      int										=       params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

							__snake_case		:      Dict										=       params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
							return wi, wo







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]







def     lowerCAmelCase_					( __lowerCamelCase							,     *, __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False     ):
							__snake_case		:      str										=       traverse_util.flatten_dict(variables["target"]     )
							__snake_case		:      Optional[int]										=       {"/".join(__lowerCamelCase     ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__snake_case		:      Optional[int]										=       "encoder/encoder/mlp/wi_0/kernel" in old
							print("Split MLP:"							,     __lowerCamelCase     )

							__snake_case		:      Dict										=       collections.OrderedDict()

							# Shared embeddings.
							__snake_case		:      List[str]										=       old["token_embedder/embedding"]

							# Encoder.
							for i in range(__lowerCamelCase     ):
														# Block i, layer 0 (Self Attention).
														__snake_case		:      List[Any]										=       tax_layer_norm_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "encoder"							,     "pre_attention_layer_norm"     )
														__snake_case		:      Dict										=       tax_attention_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "encoder"							,     "attention"     )
														__snake_case		:      List[Any]										=       layer_norm
														__snake_case		:      Union[str, Any]										=       k.T
														__snake_case		:      str										=       o.T
														__snake_case		:      Optional[Any]										=       q.T
														__snake_case		:      int										=       v.T

														# Block i, layer 1 (MLP).
														__snake_case		:      Any										=       tax_layer_norm_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "encoder"							,     "pre_mlp_layer_norm"     )
														__snake_case		:      Dict										=       tax_mlp_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "encoder"							,     __lowerCamelCase     )
														__snake_case		:      Optional[int]										=       layer_norm
														if split_mlp_wi:
																					__snake_case		:      int										=       wi[0].T
																					__snake_case		:      Any										=       wi[1].T
														else:
																					__snake_case		:      Any										=       wi.T
														__snake_case		:      Optional[int]										=       wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					__snake_case		:      str										=       tax_relpos_bias_lookup(
																					    __lowerCamelCase							,     __lowerCamelCase							,     "encoder"     ).T

							__snake_case		:      Any										=       old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														__snake_case		:      Optional[int]										=       tax_relpos_bias_lookup(
														    __lowerCamelCase							,     0							,     "encoder"     ).T
														__snake_case		:      Any										=       tax_relpos_bias_lookup(
														    __lowerCamelCase							,     0							,     "decoder"     ).T

							if not is_encoder_only:
														# Decoder.
														for i in range(__lowerCamelCase     ):
																					# Block i, layer 0 (Self Attention).
																					__snake_case		:      Union[str, Any]										=       tax_layer_norm_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     "pre_self_attention_layer_norm"     )
																					__snake_case		:      Any										=       tax_attention_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     "self_attention"     )
																					__snake_case		:      Any										=       layer_norm
																					__snake_case		:      Union[str, Any]										=       k.T
																					__snake_case		:      Optional[int]										=       o.T
																					__snake_case		:      Any										=       q.T
																					__snake_case		:      Any										=       v.T

																					# Block i, layer 1 (Cross Attention).
																					__snake_case		:      Tuple										=       tax_layer_norm_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     "pre_cross_attention_layer_norm"     )
																					__snake_case		:      Optional[Any]										=       tax_attention_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     "encoder_decoder_attention"     )
																					__snake_case		:      str										=       layer_norm
																					__snake_case		:      str										=       k.T
																					__snake_case		:      List[str]										=       o.T
																					__snake_case		:      List[str]										=       q.T
																					__snake_case		:      Any										=       v.T

																					# Block i, layer 2 (MLP).
																					__snake_case		:      Dict										=       tax_layer_norm_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     "pre_mlp_layer_norm"     )
																					__snake_case		:      Union[str, Any]										=       tax_mlp_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"							,     __lowerCamelCase     )
																					__snake_case		:      Union[str, Any]										=       layer_norm
																					if split_mlp_wi:
																												__snake_case		:      List[Any]										=       wi[0].T
																												__snake_case		:      str										=       wi[1].T
																					else:
																												__snake_case		:      str										=       wi.T
																					__snake_case		:      Dict										=       wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												__snake_case		:      Optional[Any]										=       tax_relpos_bias_lookup(__lowerCamelCase							,     __lowerCamelCase							,     "decoder"     ).T

														__snake_case		:      List[Any]										=       old["decoder/decoder_norm/scale"]

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					__snake_case		:      Dict										=       old["decoder/logits_dense/kernel"].T

							return new







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      int										=       collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														__snake_case		:      Optional[Any]										=       state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__snake_case		:      List[str]										=       state_dict["shared.weight"]

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print("Using shared word embeddings as lm_head."     )
																					__snake_case		:      Union[str, Any]										=       state_dict["shared.weight"]

							return state_dict







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      str										=       checkpoints.load_tax_checkpoint(__lowerCamelCase     )
							__snake_case		:      Any										=       convert_tax_to_pytorch(
							    __lowerCamelCase							,     num_layers=config.num_layers							,     is_encoder_only=__lowerCamelCase							,     scalable_attention=__lowerCamelCase     )
							__snake_case		:      List[str]										=       make_state_dict(__lowerCamelCase							,     __lowerCamelCase     )
							model.load_state_dict(__lowerCamelCase							,     strict=__lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False							,     __lowerCamelCase = False							,     ):
							__snake_case		:      Optional[int]										=       MTaConfig.from_json_file(__lowerCamelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														__snake_case		:      Optional[Any]										=       UMTaEncoderModel(__lowerCamelCase     )
							else:
														__snake_case		:      List[Any]										=       UMTaForConditionalGeneration(__lowerCamelCase     )

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							model.save_pretrained(__lowerCamelCase     )

							# Verify that we can load the checkpoint.
							model.from_pretrained(__lowerCamelCase     )
							print("Done"     )


if __name__ == "__main__":
			_snake_case					:		Optional[int]      =    argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			parser.add_argument(
			    "--scalable_attention",
			    action="store_true",
			    help="Whether the model uses scaled attention (umt5 model)",
			    default=False,
			)
			_snake_case					:		Dict      =    parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)





from scipy.stats import spearmanr

import datasets


_snake_case					:		Optional[Any]      =    "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

_snake_case					:		Dict      =    "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

_snake_case					:		Optional[Any]      =    R"\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n"

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




							"""simple docstring"""


							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														return datasets.MetricInfo(
														    description=_DESCRIPTION   ,     citation=_CITATION   ,     inputs_description=_KWARGS_DESCRIPTION   ,     features=datasets.Features(
														        {
														            "predictions": datasets.Value("float"    ),
														            "references": datasets.Value("float"    ),
														        }    )   ,     reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]   ,     )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Tuple=False    )     ->   Dict:
														__snake_case		:      Optional[int]										=       spearmanr(lowerCamelCase   ,     lowerCamelCase    )
														if return_pvalue:
																					return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
														else:
																					return {"spearmanr": results[0]}

