


"""simple docstring"""


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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__="attention"	)      ->		List[str]:







		"""simple docstring"""





		A					       = np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]	)
		A					       = k_tmp.reshape(k_tmp.shape[0] ,			k_tmp.shape[1] * k_tmp.shape[2]	)
		A					       = np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]	)
		A					       = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,			o_tmp.shape[2]	)
		A					       = np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]	)
		A					       = q_tmp.reshape(q_tmp.shape[0] ,			q_tmp.shape[1] * q_tmp.shape[2]	)
		A					       = np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]	)
		A					       = v_tmp.reshape(v_tmp.shape[0] ,			v_tmp.shape[1] * v_tmp.shape[2]	)
		return k, o, q, v







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=False	)      ->		int:







		"""simple docstring"""





		if split_mlp_wi:
				A					       = params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
				A					       = params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
				A					       = (wi_a, wi_a)
		else:
				A					       = params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

		A					       = params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
		return wi, wo







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Any:







		"""simple docstring"""





		return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]







def  __snake_case       (   UpperCamelCase__ ,			*, UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ = False	)      ->		List[str]:







		"""simple docstring"""





		A					       = traverse_util.flatten_dict(variables['target']	)
		A					       = {"/".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
		A					       = "encoder/encoder/mlp/wi_0/kernel" in old
		print('Split MLP:' ,			lowerCamelCase__	)

		A					       = collections.OrderedDict()

		# Shared embeddings.
		A					       = old["token_embedder/embedding"]

		# Encoder.
		for i in range(lowerCamelCase__	):
				# Block i, layer 0 (Self Attention).
				A					       = tax_layer_norm_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'encoder' ,			'pre_attention_layer_norm'	)
				A					       = tax_attention_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'encoder' ,			'attention'	)
				A					       = layer_norm
				A					       = k.T
				A					       = o.T
				A					       = q.T
				A					       = v.T

				# Block i, layer 1 (MLP).
				A					       = tax_layer_norm_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'encoder' ,			'pre_mlp_layer_norm'	)
				A					       = tax_mlp_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'encoder' ,			lowerCamelCase__	)
				A					       = layer_norm
				if split_mlp_wi:
						A					       = wi[0].T
						A					       = wi[1].T
				else:
						A					       = wi.T
				A					       = wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						A					       = tax_relpos_bias_lookup(
						    lowerCamelCase__ ,			lowerCamelCase__ ,			'encoder'	).T

		A					       = old["encoder/encoder_norm/scale"]

		if not scalable_attention:
				A					       = tax_relpos_bias_lookup(
				    lowerCamelCase__ ,			0 ,			'encoder'	).T
				A					       = 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).
						A					       = tax_layer_norm_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			'pre_self_attention_layer_norm'	)
						A					       = tax_attention_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			'self_attention'	)
						A					       = layer_norm
						A					       = k.T
						A					       = o.T
						A					       = q.T
						A					       = v.T

						# Block i, layer 1 (Cross Attention).
						A					       = tax_layer_norm_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			'pre_cross_attention_layer_norm'	)
						A					       = tax_attention_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			'encoder_decoder_attention'	)
						A					       = layer_norm
						A					       = k.T
						A					       = o.T
						A					       = q.T
						A					       = v.T

						# Block i, layer 2 (MLP).
						A					       = tax_layer_norm_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			'pre_mlp_layer_norm'	)
						A					       = tax_mlp_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder' ,			lowerCamelCase__	)
						A					       = layer_norm
						if split_mlp_wi:
								A					       = wi[0].T
								A					       = wi[1].T
						else:
								A					       = wi.T
						A					       = wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								A					       = tax_relpos_bias_lookup(lowerCamelCase__ ,			lowerCamelCase__ ,			'decoder'	).T

				A					       = 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:
						A					       = old["decoder/logits_dense/kernel"].T

		return new







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = 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:
				A					       = state_dict["shared.weight"]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						A					       = 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.'	)
						A					       = state_dict["shared.weight"]

		return state_dict







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = checkpoints.load_tax_checkpoint(lowerCamelCase__	)
		A					       = convert_tax_to_pytorch(
		    lowerCamelCase__ ,			num_layers=config.num_layers ,			is_encoder_only=lowerCamelCase__ ,			scalable_attention=lowerCamelCase__	)
		A					       = make_state_dict(lowerCamelCase__ ,			lowerCamelCase__	)
		model.load_state_dict(lowerCamelCase__ ,			strict=lowerCamelCase__	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ = False ,			UpperCamelCase__ = False ,			)      ->		List[Any]:







		"""simple docstring"""





		A					       = 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:
				A					       = UMTaEncoderModel(lowerCamelCase__	)
		else:
				A					       = 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__":
	UpperCamelCase						:				Tuple             =    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,
	)
	UpperCamelCase						:				Tuple             =    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,
	)









"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


UpperCamelCase						:				Optional[Any]             =      logging.get_logger(__name__)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







						def __init__(							self   :    str			,			**_lowercase   :    Union[str, Any]		):
								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												A					       = deprecated_arg[3:]
												A					       = not kwargs.pop(_lowercase		)
												logger.warning(
												    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
												    f' {positive_arg}={kwargs[positive_arg]}'		)
								A					       = kwargs.pop('tpu_name'			,			self.tpu_name		)
								A					       = kwargs.pop('device_idx'			,			self.device_idx		)
								A					       = kwargs.pop('eager_mode'			,			self.eager_mode		)
								A					       = kwargs.pop('use_xla'			,			self.use_xla		)
								super().__init__(**_lowercase		)

						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Name of TPU"""}							,    )
						lowerCAmelCase       =     field(
						    default=0							,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}							,    )
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    metadata={"""help""": """Benchmark models in eager model."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    }							,    )







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								requires_backends(self			,			['tf']		)
								if self.is_tpu:
										tf.config.experimental_connect_to_cluster(self._setup_tpu		)
										tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

										A					       = tf.distribute.TPUStrategy(self._setup_tpu		)
								else:
										# currently no multi gpu is allowed
										if self.is_gpu:
												# TODO: Currently only single GPU is supported
												tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,			'GPU'		)
												A					       = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'		)
										else:
												tf.config.set_visible_devices([]			,			'GPU'		)  # disable GPU
												A					       = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'		)

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







						@property
						def __a						(							self   :    Dict		):
								return self.n_gpu > 0





