


"""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   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Tuple						,							lowercase  :  Optional[Any]						,							lowercase  :  Dict="attention"			)     ->						Any:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
    snake_case   :				Tuple				 =   params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
    snake_case   :				int				 =   params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
    snake_case   :				Optional[Any]				 =   params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Any						,							lowercase  :  List[Any]						,							lowercase  :  Dict=False			)     ->						Union[str, Any]:


    """simple docstring"""





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

    snake_case   :				Optional[Any]				 =   params[F'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Optional[Any]						,							lowercase  :  int						,							lowercase  :  List[Any]			)     ->						str:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							*, lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   traverse_util.flatten_dict(variables["target"]			)
    snake_case   :				Optional[int]				 =   {'/'.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
    snake_case   :				Any				 =   'encoder/layers_0/mlp/wi_0/kernel' in old
    print("Split MLP:"						,							lowerCamelCase_			)

    snake_case   :				Optional[int]				 =   collections.OrderedDict()

    # Shared embeddings.
    snake_case   :				Tuple				 =   old['token_embedder/embedding']

    # Encoder.
    for i in range(lowerCamelCase_			):
        # Block i, layer 0 (Self Attention).
        snake_case   :				Tuple				 =   tax_layer_norm_lookup(lowerCamelCase_						,							lowerCamelCase_						,							"encoder"						,							"pre_attention_layer_norm"			)
        snake_case   :				Dict				 =   tax_attention_lookup(lowerCamelCase_						,							lowerCamelCase_						,							"encoder"						,							"attention"			)
        snake_case   :				Any				 =   layer_norm
        snake_case   :				Dict				 =   k.T
        snake_case   :				str				 =   o.T
        snake_case   :				Union[str, Any]				 =   q.T
        snake_case   :				List[Any]				 =   v.T

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

    snake_case   :				Optional[Any]				 =   old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    snake_case   :				Optional[int]				 =   old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(lowerCamelCase_			):
            # Block i, layer 0 (Self Attention).
            snake_case   :				int				 =   tax_layer_norm_lookup(lowerCamelCase_						,							lowerCamelCase_						,							"decoder"						,							"pre_self_attention_layer_norm"			)
            snake_case   :				Dict				 =   tax_attention_lookup(lowerCamelCase_						,							lowerCamelCase_						,							"decoder"						,							"self_attention"			)
            snake_case   :				Dict				 =   layer_norm
            snake_case   :				Tuple				 =   k.T
            snake_case   :				Optional[int]				 =   o.T
            snake_case   :				Dict				 =   q.T
            snake_case   :				Optional[Any]				 =   v.T

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

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

        snake_case   :				Tuple				 =   old['decoder/decoder_norm/scale']
        snake_case   :				List[str]				 =   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   :				List[str]				 =   old['decoder/logits_dense/kernel'].T

    return new







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  List[Any]			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				List[str]				 =   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   :				Optional[int]				 =   state_dict['shared.weight']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            snake_case   :				str				 =   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   :				Union[str, Any]				 =   state_dict['shared.weight']

    return state_dict







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Dict						,							lowercase  :  Tuple						,							lowercase  :  str			)     ->						int:


    """simple docstring"""





    snake_case   :				Tuple				 =   checkpoints.load_tax_checkpoint(lowerCamelCase_			)
    snake_case   :				Union[str, Any]				 =   convert_tax_to_pytorch(lowerCamelCase_						,							num_layers=config.num_layers						,							is_encoder_only=lowerCamelCase_			)
    snake_case   :				int				 =   make_state_dict(lowerCamelCase_						,							lowerCamelCase_			)
    model.load_state_dict(lowerCamelCase_						,							strict=lowerCamelCase_			)







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  str						,							lowercase  :  int						,							lowercase  :  Dict = False			)     ->						Dict:


    """simple docstring"""





    snake_case   :				List[Any]				 =   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:
        snake_case   :				Optional[int]				 =   TaEncoderModel(lowerCamelCase_			)
    else:
        snake_case   :				str				 =   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__":
  __snake_case             =   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
  )
  __snake_case             =   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 unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer







@require_tf
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
       for a, b in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertAlmostEqual(UpperCamelCase__						,     UpperCamelCase__						,     delta=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(UpperCamelCase__						):
           accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step						,     3						)
       self.assertEqual(len(accumulator.gradients						)						,     1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [-2.0, 5.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [0.0, 0.0]						,     tol=1e-2						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   None
       ops.enable_eager_execution_internal()
       snake_case   :				str				 =   tf.config.list_physical_devices("CPU"						)
       if len(UpperCamelCase__						) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]						,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       snake_case   :				Optional[int]				 =   tf.config.list_logical_devices(device_type="CPU"						)
       snake_case   :				List[str]				 =   tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
           snake_case   :				int				 =   GradientAccumulator()
           snake_case   :				int				 =   tf.Variable([4.0, 3.0]						)
           snake_case ,snake_case   :				Any				 =   create_optimizer(5e-5						,     10						,     5						)
           snake_case   :				Tuple				 =   tf.Variable([0.0, 0.0]						,     trainable=UpperCamelCase__						)

       def accumulate_on_replica(UpperCamelCase__						):
           accumulator([gradient]						)

       def apply_on_replica():
           optimizer.apply_gradients(list(zip(accumulator.gradients						,     [variable]						)						)						)

       @tf.function
       def accumulate(UpperCamelCase__						,     UpperCamelCase__						):
           with strategy.scope():
               snake_case   :				Union[str, Any]				 =   strategy.experimental_local_results(UpperCamelCase__						)
               local_variables[0].assign(UpperCamelCase__						)
               local_variables[1].assign(UpperCamelCase__						)
               strategy.run(UpperCamelCase__						,     args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
           with strategy.scope():
               strategy.run(UpperCamelCase__						)

       def _check_local_values(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				List[Any]				 =   strategy.experimental_local_results(accumulator._gradients[0]						)
           self.assertListAlmostEqual(values[0].value()						,     UpperCamelCase__						,     tol=1e-2						)
           self.assertListAlmostEqual(values[1].value()						,     UpperCamelCase__						,     tol=1e-2						)

       accumulate([1.0, 2.0]						,     [-1.0, 1.0]						)
       accumulate([3.0, -1.0]						,     [-1.0, -1.0]						)
       accumulate([-2.0, 2.0]						,     [3.0, -2.0]						)
       self.assertEqual(accumulator.step						,     3						)
       _check_local_values([2.0, 3.0]						,     [1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,     [4.0, 3.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       _check_local_values([0.0, 0.0]						,     [0.0, 0.0]						)





