




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 			__magic_name__				( A		:					Optional[int],  A		:					Any,  A		:					Any,  A		:					int="attention"   ):






			'''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 			__magic_name__				( A		:					int,  A		:					Any,  A		:					int,  A		:					Tuple=False   ):






			'''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 			__magic_name__				( A		:					Union[str, Any],  A		:					Dict,  A		:					Any,  A		:					Optional[Any]   ):






			'''simple docstring'''







			return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]




def 			__magic_name__				( A		:					dict,  *, A		:					int,  A		:					bool   ):






			'''simple docstring'''







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

			a    =			collections.OrderedDict()

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

			# Encoder.
			for i in range(A   ):
						# Block i, layer 0 (Self Attention).
						a    =			tax_layer_norm_lookup(A,  A,  "encoder",  "pre_attention_layer_norm"   )
						a    ,		a    ,		a    ,		a    =			tax_attention_lookup(A,  A,  "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(A,  A,  "encoder",  "pre_mlp_layer_norm"   )
						a    ,		a    =			tax_mlp_lookup(A,  A,  "encoder",  A   )
						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(A   ):
									# Block i, layer 0 (Self Attention).
									a    =			tax_layer_norm_lookup(A,  A,  "decoder",  "pre_self_attention_layer_norm"   )
									a    ,		a    ,		a    ,		a    =			tax_attention_lookup(A,  A,  "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(A,  A,  "decoder",  "pre_cross_attention_layer_norm"   )
									a    ,		a    ,		a    ,		a    =			tax_attention_lookup(A,  A,  "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(A,  A,  "decoder",  "pre_mlp_layer_norm"   )
									a    ,		a    =			tax_mlp_lookup(A,  A,  "decoder",  A   )
									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 			__magic_name__				( A		:					Dict,  A		:					bool   ):






			'''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 			__magic_name__				( A		:					List[Any],  A		:					Optional[int],  A		:					str,  A		:					str   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					Any,  A		:					Tuple,  A		:					Optional[Any],  A		:					bool = False   ):






			'''simple docstring'''







			a    =			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:
						a    =			TaEncoderModel(A   )
			else:
						a    =			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__":
		__lowerCAmelCase		:				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
		)
		__lowerCAmelCase		:				str       			=		parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)








"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__magic_name__       =			logging.get_logger(__name__)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				warnings.warn(
				    """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
				    """ use YolosImageProcessor instead."""							,  lowerCAmelCase__							,  )
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




