


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   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     str="attention"						) ->   Tuple:





	"""simple docstring"""







	A								=				params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
	A								=				params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
	A								=				params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
	A								=				params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
	return k, o, q, v





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     str=False						) ->   List[str]:





	"""simple docstring"""







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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   Union[str, Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					*, UpperCamelCase__:     int							,					UpperCamelCase__:     bool						) ->   Any:





	"""simple docstring"""







	A								=				traverse_util.flatten_dict(variables["""target"""]						)
	A								=				{"""/""".join(UpperCamelCase__						): 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/layers_0/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""							,					UpperCamelCase__						)

	A								=				collections.OrderedDict()

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

	# Encoder.
	for i in range(UpperCamelCase__						):
		# Block i, layer 0 (Self Attention).
		A								=				tax_layer_norm_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""encoder"""							,					"""pre_attention_layer_norm"""						)
		A					,			A					,			A					,			A								=				tax_attention_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""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(UpperCamelCase__							,					UpperCamelCase__							,					"""encoder"""							,					"""pre_mlp_layer_norm"""						)
		A					,			A								=				tax_mlp_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""encoder"""							,					UpperCamelCase__						)
		A								=				layer_norm
		if split_mlp_wi:
			A								=				wi[0].T
			A								=				wi[1].T
		else:
			A								=				wi.T
		A								=				wo.T

	A								=				old[
	    """encoder/relpos_bias/rel_embedding"""
	].T
	A								=				old["""encoder/encoder_norm/scale"""]

	if not is_encoder_only:
		# Decoder.
		for i in range(UpperCamelCase__						):
			# Block i, layer 0 (Self Attention).
			A								=				tax_layer_norm_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""decoder"""							,					"""pre_self_attention_layer_norm"""						)
			A					,			A					,			A					,			A								=				tax_attention_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""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(UpperCamelCase__							,					UpperCamelCase__							,					"""decoder"""							,					"""pre_cross_attention_layer_norm"""						)
			A					,			A					,			A					,			A								=				tax_attention_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""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(UpperCamelCase__							,					UpperCamelCase__							,					"""decoder"""							,					"""pre_mlp_layer_norm"""						)
			A					,			A								=				tax_mlp_lookup(UpperCamelCase__							,					UpperCamelCase__							,					"""decoder"""							,					UpperCamelCase__						)
			A								=				layer_norm
			if split_mlp_wi:
				A								=				wi[0].T
				A								=				wi[1].T
			else:
				A								=				wi.T
			A								=				wo.T

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

	return new





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     bool						) ->   List[Any]:





	"""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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     bool = False						) ->   Any:





	"""simple docstring"""







	A								=				TaConfig.from_json_file(UpperCamelCase__						)
	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								=				TaEncoderModel(UpperCamelCase__						)
	else:
		A								=				TaForConditionalGeneration(UpperCamelCase__						)

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

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

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


if __name__ == "__main__":
				_lowercase       :      List[Any]	=	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       :      Any	=	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_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



