


"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
    import jax.numpy as jnp

    from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @slow
     def 		a__						(	self:						Any	)->    List[str]:
           lowerCamelCase					:					int        =	FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base"""	)
           lowerCamelCase					:					List[str]        =	AutoTokenizer.from_pretrained("""xlm-roberta-base"""	)
           lowerCamelCase					:					Optional[Any]        =	"""The dog is cute and lives in the garden house"""
           lowerCamelCase					:					Optional[Any]        =	jnp.array([tokenizer.encode(__a	)]	)

           lowerCamelCase					:					Tuple        =	(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
           lowerCamelCase					:					List[str]        =	jnp.array(
               [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]	)

           lowerCamelCase					:					List[Any]        =	model(__a	)["""last_hidden_state"""]
           self.assertEqual(output.shape     ,		__a	)
           # compare the actual values for a slice of last dim
           self.assertTrue(jnp.allclose(output[:, :, -1]     ,		__a     ,		atol=1e-3	)	)








"""simple docstring"""


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]=None    )   ->      Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
      lowerCamelCase					:					Dict        =	nn.Parameter(UpperCamelCase__    )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
            lowerCamelCase					:					Any        =	nn.Parameter(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Dict        =	np.asarray(weights[0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[2]    )

      set_param(
          torch_layer.self_attention.query_key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Tuple        =	np.asarray(weights[0]    )
      lowerCamelCase					:					Any        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[2]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[3]    )

      set_param(
          torch_layer.self_attention.query			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any    )   ->      Optional[Any]:
      # layernorm 1
      lowerCamelCase					:					str        =	weights[0][0][0]
      lowerCamelCase					:					Optional[int]        =	np.asarray(layer_norm_a[0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(layer_norm_a[1]    )
      set_param(
          torch_block.attention.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # lsh weights + output
      lowerCamelCase					:					List[Any]        =	weights[0][1]
      if len(UpperCamelCase__    ) < 4:
            set_layer_weights_in_torch_lsh(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )
      else:
            set_layer_weights_in_torch_local(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )

      # intermediate weighs
      lowerCamelCase					:					int        =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCamelCase__    ) == 4:
            lowerCamelCase					:					Dict        =	intermediate_weights[2]

      # layernorm 2
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[0][0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(intermediate_weights[0][1]    )
      set_param(
          torch_block.feed_forward.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate dense
      lowerCamelCase					:					Optional[Any]        =	np.asarray(intermediate_weights[1][0]    )
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(intermediate_weights[1][1]    )
      set_param(
          torch_block.feed_forward.dense.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate out
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[4][0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(intermediate_weights[4][1]    )
      set_param(
          torch_block.feed_forward.output.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      # reformer model
      lowerCamelCase					:					List[Any]        =	torch_model.reformer

      # word embeds
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(weights[1]    )
      set_param(
          torch_model_reformer.embeddings.word_embeddings			,				torch.tensor(UpperCamelCase__    )			,				)

      if isinstance(weights[3]			,				UpperCamelCase__    ):
            lowerCamelCase					:					Union[str, Any]        =	torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights    )    ):
                  lowerCamelCase					:					str        =	np.asarray(weights[3][emb_idx][0]    )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'{position_embeddings[emb_idx]} emb does not match'
                  lowerCamelCase					:					Dict        =	nn.Parameter(torch.tensor(UpperCamelCase__    )    )

      lowerCamelCase					:					int        =	weights[5]
      assert len(torch_model_reformer.encoder.layers    ) * 4 == len(
          UpperCamelCase__    ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers    ):
            lowerCamelCase					:					Dict        =	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      # output layer norm
      lowerCamelCase					:					Any        =	np.asarray(weights[7][0]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[7][1]    )
      set_param(
          torch_model_reformer.encoder.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # output embeddings
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[9][0]    )
      lowerCamelCase					:					Optional[int]        =	np.asarray(weights[9][1]    )
      set_param(
          torch_model.lm_head.decoder			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      # Initialise PyTorch model
      lowerCamelCase					:					Union[str, Any]        =	ReformerConfig.from_json_file(UpperCamelCase__    )
      print(F'Building PyTorch model from configuration: {config}'    )
      lowerCamelCase					:					str        =	ReformerModelWithLMHead(UpperCamelCase__    )

      with open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )["""weights"""]

      set_model_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				config.hidden_size    )

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'    )
      torch.save(model.state_dict()			,				UpperCamelCase__    )


if __name__ == "__main__":
    __lowerCamelCase      :Dict	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCamelCase      :Optional[int]	    =		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




