






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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Any  ,						snake_case_      :							str     ):
	return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]







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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[Any]=False     ):

	if split_mlp_wi:
		snake_case__				:   Dict     =						params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
		snake_case__				:   Optional[Any]     =						params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
		snake_case__				:   Union[str, Any]     =						(wi_a, wi_a)
	else:
		snake_case__				:   Optional[Any]     =						params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							str     ):
	return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						*, snake_case_      :							List[str]  ,						snake_case_      :							Dict  ,						snake_case_      :							int = False     ):
	snake_case__				:   int     =						traverse_util.flatten_dict(variables["target"]     )
	snake_case__				:   str     =						{"/".join(lowercase_     ): 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__				:   Union[str, Any]     =						"encoder/encoder/mlp/wi_0/kernel" in old
	print("Split MLP:"  ,						lowercase_     )

	snake_case__				:   List[Any]     =						collections.OrderedDict()

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

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

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

	snake_case__				:   str     =						old["encoder/encoder_norm/scale"]

	if not scalable_attention:
		snake_case__				:   Any     =						tax_relpos_bias_lookup(
		    lowercase_  ,						0  ,						"encoder"     ).T
		snake_case__				:   str     =						tax_relpos_bias_lookup(
		    lowercase_  ,						0  ,						"decoder"     ).T

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

			# Block i, layer 1 (Cross Attention).
			snake_case__				:   Dict     =						tax_layer_norm_lookup(lowercase_  ,						lowercase_  ,						"decoder"  ,						"pre_cross_attention_layer_norm"     )
			snake_case__				:   int     =						tax_attention_lookup(lowercase_  ,						lowercase_  ,						"decoder"  ,						"encoder_decoder_attention"     )
			snake_case__				:   List[str]     =						layer_norm
			snake_case__				:   str     =						k.T
			snake_case__				:   List[Any]     =						o.T
			snake_case__				:   int     =						q.T
			snake_case__				:   List[Any]     =						v.T

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

			if scalable_attention:
				# convert the rel_embedding of each layer
				snake_case__				:   Optional[int]     =						tax_relpos_bias_lookup(lowercase_  ,						lowercase_  ,						"decoder"     ).T

		snake_case__				:   Tuple     =						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__				:   Any     =						old["decoder/logits_dense/kernel"].T

	return new







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Dict     ):
	snake_case__				:   Dict     =						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__				:   str     =						state_dict["shared.weight"]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			snake_case__				:   Optional[int]     =						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__				:   int     =						state_dict["shared.weight"]

	return state_dict







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							List[str]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							int     ):
	snake_case__				:   int     =						checkpoints.load_tax_checkpoint(lowercase_     )
	snake_case__				:   List[str]     =						convert_tax_to_pytorch(
	    lowercase_  ,						num_layers=config.num_layers  ,						is_encoder_only=lowercase_  ,						scalable_attention=lowercase_     )
	snake_case__				:   Union[str, Any]     =						make_state_dict(lowercase_  ,						lowercase_     )
	model.load_state_dict(lowercase_  ,						strict=lowercase_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							List[str]  ,						snake_case_      :							int  ,						snake_case_      :							List[Any] = False  ,						snake_case_      :							Tuple = False  ,						):
	snake_case__				:   int     =						MTaConfig.from_json_file(lowercase_     )
	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[int]     =						UMTaEncoderModel(lowercase_     )
	else:
		snake_case__				:   Optional[int]     =						UMTaForConditionalGeneration(lowercase_     )

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

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

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


if __name__ == "__main__":
	__lowerCamelCase      :    List[str]   		=   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,
	)
	__lowerCamelCase      :    List[str]   		=   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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



