






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_="attention"):
							snake_case_				=							params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							snake_case_				=							params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							snake_case_				=							params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							snake_case_				=							params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_=False):
							if split_mlp_wi:
														snake_case_				=							params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
														snake_case_				=							params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
														snake_case_				=							(wi_a, wi_a)
							else:
														snake_case_				=							params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

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





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):
							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']





def 						__UpperCAmelCase			(     a_							,     *, a_							,     a_):
							snake_case_				=							traverse_util.flatten_dict(variables['target'])
							snake_case_				=							{'/'.join(a_): 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_				=							'encoder/layers_0/mlp/wi_0/kernel' in old
							print('Split MLP:'							,     a_)

							snake_case_				=							collections.OrderedDict()

							# Shared embeddings.
							snake_case_				=							old['token_embedder/embedding']

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

														# Block i, layer 1 (MLP).
														snake_case_				=							tax_layer_norm_lookup(a_							,     a_							,     'encoder'							,     'pre_mlp_layer_norm')
														snake_case_			,		snake_case_				=							tax_mlp_lookup(a_							,     a_							,     'encoder'							,     a_)
														snake_case_				=							layer_norm
														if split_mlp_wi:
																					snake_case_				=							wi[0].T
																					snake_case_				=							wi[1].T
														else:
																					snake_case_				=							wi.T
														snake_case_				=							wo.T

							snake_case_				=							old[
							    'encoder/relpos_bias/rel_embedding'
							].T
							snake_case_				=							old['encoder/encoder_norm/scale']

							if not is_encoder_only:
														# Decoder.
														for i in range(a_):
																					# Block i, layer 0 (Self Attention).
																					snake_case_				=							tax_layer_norm_lookup(a_							,     a_							,     'decoder'							,     'pre_self_attention_layer_norm')
																					snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							tax_attention_lookup(a_							,     a_							,     'decoder'							,     'self_attention')
																					snake_case_				=							layer_norm
																					snake_case_				=							k.T
																					snake_case_				=							o.T
																					snake_case_				=							q.T
																					snake_case_				=							v.T

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

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

														snake_case_				=							old['decoder/decoder_norm/scale']
														snake_case_				=							old[
														    'decoder/relpos_bias/rel_embedding'
														].T

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					snake_case_				=							old['decoder/logits_dense/kernel'].T

							return new





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							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_				=							state_dict['shared.weight']

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

							return state_dict





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):
							snake_case_				=							checkpoints.load_tax_checkpoint(a_)
							snake_case_				=							convert_tax_to_pytorch(a_							,     num_layers=config.num_layers							,     is_encoder_only=a_)
							snake_case_				=							make_state_dict(a_							,     a_)
							model.load_state_dict(a_							,     strict=a_)





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_ = False):
							snake_case_				=							TaConfig.from_json_file(a_)
							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_				=							TaEncoderModel(a_)
							else:
														snake_case_				=							TaForConditionalGeneration(a_)

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(a_)
							print('Done')


if __name__ == "__main__":
	lowercase  							=					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
	)
	lowercase  							=					parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase  							=					{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





