"""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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[Any]:
  # set parameter of one layer
  assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
  a__:					List[str]      							=     nn.Parameter(_SCREAMING_SNAKE_CASE       )
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
    a__:					Optional[Any]      							=     nn.Parameter(_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  # set torch weights for 1-to-1 comparison
  a__:					Optional[int]      							=     np.asarray(weights[0]       )
  a__:					Tuple      							=     np.asarray(weights[1]       )
  a__:					Optional[int]      							=     np.asarray(weights[2]       )

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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  # set torch weights for 1-to-1 comparison
  a__:					int      							=     np.asarray(weights[0]       )
  a__:					Tuple      							=     np.asarray(weights[1]       )
  a__:					List[Any]      							=     np.asarray(weights[2]       )
  a__:					Any      							=     np.asarray(weights[3]       )

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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  # layernorm 1
  a__:					Tuple      							=     weights[0][0][0]
  a__:					Tuple      							=     np.asarray(layer_norm_a[0]       )
  a__:					Optional[int]      							=     np.asarray(layer_norm_a[1]       )
  set_param(
      torch_block.attention.layer_norm		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					)

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

  # intermediate weighs
  a__:					Tuple      							=     weights[2][0][1][2]

  # Chunked Feed Forward
  if len(_SCREAMING_SNAKE_CASE       ) == 4:
    a__:					Optional[int]      							=     intermediate_weights[2]

  # layernorm 2
  a__:					Union[str, Any]      							=     np.asarray(intermediate_weights[0][0]       )
  a__:					List[str]      							=     np.asarray(intermediate_weights[0][1]       )
  set_param(
      torch_block.feed_forward.layer_norm		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					)

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

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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  # reformer model
  a__:					List[Any]      							=     torch_model.reformer

  # word embeds
  a__:					str      							=     np.asarray(weights[1]       )
  set_param(
      torch_model_reformer.embeddings.word_embeddings		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					)

  if isinstance(weights[3]		,					_SCREAMING_SNAKE_CASE       ):
    a__:					Any      							=     torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights       )       ):
      a__:					Tuple      							=     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'
      a__:					str      							=     nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE       )       )

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

  # output layer norm
  a__:					Tuple      							=     np.asarray(weights[7][0]       )
  a__:					Optional[Any]      							=     np.asarray(weights[7][1]       )
  set_param(
      torch_model_reformer.encoder.layer_norm		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					)

  # output embeddings
  a__:					Dict      							=     np.asarray(weights[9][0]       )
  a__:					Optional[Any]      							=     np.asarray(weights[9][1]       )
  set_param(
      torch_model.lm_head.decoder		,					torch.tensor(_SCREAMING_SNAKE_CASE       ).transpose(0		,					1       ).contiguous()		,					torch.tensor(_SCREAMING_SNAKE_CASE       )		,					)







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  # Initialise PyTorch model
  a__:					Tuple      							=     ReformerConfig.from_json_file(_SCREAMING_SNAKE_CASE       )
  print(F'Building PyTorch model from configuration: {config}'       )
  a__:					List[Any]      							=     ReformerModelWithLMHead(_SCREAMING_SNAKE_CASE       )

  with open(_SCREAMING_SNAKE_CASE		,					'rb'       ) as f:
    a__:					str      							=     pickle.load(_SCREAMING_SNAKE_CASE       )['weights']

  set_model_weights_in_torch(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					config.hidden_size       )

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


if __name__ == "__main__":
 lowercase__ 	=     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.'
 )
 lowercase__ 	=     parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



"""simple docstring"""
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
 import jax

 from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
 from transformers.models.bert.modeling_flax_bert import FlaxBertModel
 from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class        __snake_case	(					unittest.TestCase    ):



      @slow
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        for model_name in ["bert-base-cased", "bert-large-uncased"]:
          with self.subTest(lowercase):
            a__:					Optional[Any]      							=     AutoConfig.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)

            a__:					Any      							=     FlaxAutoModel.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        for model_name in ["roberta-base", "roberta-large"]:
          with self.subTest(lowercase):
            a__:					str      							=     AutoConfig.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)

            a__:					Any      							=     FlaxAutoModel.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for model_name in ["bert-base-cased", "bert-large-uncased"]:
          a__:					Dict      							=     AutoTokenizer.from_pretrained(lowercase)
          a__:					Union[str, Any]      							=     FlaxBertModel.from_pretrained(lowercase)
          a__:					List[str]      							=     tokenizer('Do you support jax jitted function?'   ,      return_tensors=TensorType.JAX)

          @jax.jit
          def eval(**lowercase):
            return model(**lowercase)

          eval(**lowercase).block_until_ready()



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for model_name in ["roberta-base", "roberta-large"]:
          a__:					Optional[Any]      							=     AutoTokenizer.from_pretrained(lowercase)
          a__:					Any      							=     FlaxRobertaModel.from_pretrained(lowercase)
          a__:					int      							=     tokenizer('Do you support jax jitted function?'   ,      return_tensors=TensorType.JAX)

          @jax.jit
          def eval(**lowercase):
            return model(**lowercase)

          eval(**lowercase).block_until_ready()



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      'bert-base is not a local folder and is not a valid model identifier'):
          a__:					str      							=     FlaxAutoModel.from_pretrained('bert-base')



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained(lowercase   ,      revision='aaaaaa')



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'   ,      ):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model')



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        with self.assertRaisesRegex(lowercase   ,      'Use `from_pt=True` to load this model'):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')


