


'''simple docstring'''







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"    )							->		List[Any]:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A=False    )							->		Tuple:


      '''simple docstring'''



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

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







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A    )							->		Tuple:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A      ,			*, __A      ,			__A    )							->		List[Any]:


      '''simple docstring'''



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

      UpperCamelCase__										=				collections.OrderedDict()

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

      # Encoder.
      for i in range(lowerCAmelCase__    ):
            # Block i, layer 0 (Self Attention).
            UpperCamelCase__										=				tax_layer_norm_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"encoder"      ,			"pre_attention_layer_norm"    )
            UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				tax_attention_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"encoder"      ,			"attention"    )
            UpperCamelCase__										=				layer_norm
            UpperCamelCase__										=				k.T
            UpperCamelCase__										=				o.T
            UpperCamelCase__										=				q.T
            UpperCamelCase__										=				v.T

            # Block i, layer 1 (MLP).
            UpperCamelCase__										=				tax_layer_norm_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"encoder"      ,			"pre_mlp_layer_norm"    )
            UpperCamelCase__					,	UpperCamelCase__										=				tax_mlp_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"encoder"      ,			lowerCAmelCase__    )
            UpperCamelCase__										=				layer_norm
            if split_mlp_wi:
                  UpperCamelCase__										=				wi[0].T
                  UpperCamelCase__										=				wi[1].T
            else:
                  UpperCamelCase__										=				wi.T
            UpperCamelCase__										=				wo.T

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

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

                  # Block i, layer 1 (Cross Attention).
                  UpperCamelCase__										=				tax_layer_norm_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"decoder"      ,			"pre_cross_attention_layer_norm"    )
                  UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				tax_attention_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"decoder"      ,			"encoder_decoder_attention"    )
                  UpperCamelCase__										=				layer_norm
                  UpperCamelCase__										=				k.T
                  UpperCamelCase__										=				o.T
                  UpperCamelCase__										=				q.T
                  UpperCamelCase__										=				v.T

                  # Block i, layer 2 (MLP).
                  UpperCamelCase__										=				tax_layer_norm_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"decoder"      ,			"pre_mlp_layer_norm"    )
                  UpperCamelCase__					,	UpperCamelCase__										=				tax_mlp_lookup(lowerCAmelCase__      ,			lowerCAmelCase__      ,			"decoder"      ,			lowerCAmelCase__    )
                  UpperCamelCase__										=				layer_norm
                  if split_mlp_wi:
                        UpperCamelCase__										=				wi[0].T
                        UpperCamelCase__										=				wi[1].T
                  else:
                        UpperCamelCase__										=				wi.T
                  UpperCamelCase__										=				wo.T

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

      return new







def _UpperCamelCase       (     __A      ,			__A    )							->		Tuple:


      '''simple docstring'''



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

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

      return state_dict







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A    )							->		int:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A = False    )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__										=				TaConfig.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:
            UpperCamelCase__										=				TaEncoderModel(lowerCAmelCase__    )
      else:
            UpperCamelCase__										=				TaForConditionalGeneration(lowerCAmelCase__    )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(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__":
  a__  :					Dict        =       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
  )
  a__  :					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"""






from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	'efficientnet'

      def __init__(						self		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 600		,       __lowerCAmelCase = 2.0		,       __lowerCAmelCase = 3.1		,       __lowerCAmelCase = 8		,       __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]		,       __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]		,       __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]		,       __lowerCAmelCase = []		,       __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]		,       __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]		,       __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]		,       __lowerCAmelCase = 0.2_5		,       __lowerCAmelCase = "swish"		,       __lowerCAmelCase = 2560		,       __lowerCAmelCase = "mean"		,       __lowerCAmelCase = 0.0_2		,       __lowerCAmelCase = 0.0_0_1		,       __lowerCAmelCase = 0.9_9		,       __lowerCAmelCase = 0.5		,       __lowerCAmelCase = 0.2		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

            lowercase														=	num_channels
            lowercase														=	image_size
            lowercase														=	width_coefficient
            lowercase														=	depth_coefficient
            lowercase														=	depth_divisor
            lowercase														=	kernel_sizes
            lowercase														=	in_channels
            lowercase														=	out_channels
            lowercase														=	depthwise_padding
            lowercase														=	strides
            lowercase														=	num_block_repeats
            lowercase														=	expand_ratios
            lowercase														=	squeeze_expansion_ratio
            lowercase														=	hidden_act
            lowercase														=	hidden_dim
            lowercase														=	pooling_type
            lowercase														=	initializer_range
            lowercase														=	batch_norm_eps
            lowercase														=	batch_norm_momentum
            lowercase														=	dropout_rate
            lowercase														=	drop_connect_rate
            lowercase														=	sum(__lowerCAmelCase						) * 4






class  _A						(       lowerCAmelCase	):
      snake_case__			:   Dict							  =	version.parse('1.11'	)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]						)







      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 1E-5



