



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__      (		_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			Dict=None				):
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
   __lowerCamelCase    				=     nn.Parameter(_UpperCamelCase				)
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
      __lowerCamelCase    				=     nn.Parameter(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			int				):
   # set torch weights for 1-to-1 comparison
   __lowerCamelCase    				=     np.asarray(weights[0]				)
   __lowerCamelCase    				=     np.asarray(weights[1]				)
   __lowerCamelCase    				=     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 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Any				):
   # set torch weights for 1-to-1 comparison
   __lowerCamelCase    				=     np.asarray(weights[0]				)
   __lowerCamelCase    				=     np.asarray(weights[1]				)
   __lowerCamelCase    				=     np.asarray(weights[2]				)
   __lowerCamelCase    				=     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 		a__      (		_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Optional[Any]				):
   # layernorm 1
   __lowerCamelCase    				=     weights[0][0][0]
   __lowerCamelCase    				=     np.asarray(layer_norm_a[0]				)
   __lowerCamelCase    				=     np.asarray(layer_norm_a[1]				)
   set_param(
       torch_block.attention.layer_norm			,torch.tensor(_UpperCamelCase				)			,torch.tensor(_UpperCamelCase				)			,)

   # lsh weights + output
   __lowerCamelCase    				=     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    				=     weights[2][0][1][2]

   # Chunked Feed Forward
   if len(_UpperCamelCase				) == 4:
      __lowerCamelCase    				=     intermediate_weights[2]

   # layernorm 2
   __lowerCamelCase    				=     np.asarray(intermediate_weights[0][0]				)
   __lowerCamelCase    				=     np.asarray(intermediate_weights[0][1]				)
   set_param(
       torch_block.feed_forward.layer_norm			,torch.tensor(_UpperCamelCase				)			,torch.tensor(_UpperCamelCase				)			,)

   # intermediate dense
   __lowerCamelCase    				=     np.asarray(intermediate_weights[1][0]				)
   __lowerCamelCase    				=     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    				=     np.asarray(intermediate_weights[4][0]				)
   __lowerCamelCase    				=     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 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			Any				):
   # reformer model
   __lowerCamelCase    				=     torch_model.reformer

   # word embeds
   __lowerCamelCase    				=     np.asarray(weights[1]				)
   set_param(
       torch_model_reformer.embeddings.word_embeddings			,torch.tensor(_UpperCamelCase				)			,)

   if isinstance(weights[3]			,_UpperCamelCase				):
      __lowerCamelCase    				=     torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights				)				):
         __lowerCamelCase    				=     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    				=     nn.Parameter(torch.tensor(_UpperCamelCase				)				)

   __lowerCamelCase    				=     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    				=     trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)

   # output layer norm
   __lowerCamelCase    				=     np.asarray(weights[7][0]				)
   __lowerCamelCase    				=     np.asarray(weights[7][1]				)
   set_param(
       torch_model_reformer.encoder.layer_norm			,torch.tensor(_UpperCamelCase				)			,torch.tensor(_UpperCamelCase				)			,)

   # output embeddings
   __lowerCamelCase    				=     np.asarray(weights[9][0]				)
   __lowerCamelCase    				=     np.asarray(weights[9][1]				)
   set_param(
       torch_model.lm_head.decoder			,torch.tensor(_UpperCamelCase				).transpose(0			,1				).contiguous()			,torch.tensor(_UpperCamelCase				)			,)





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			str			,_UpperCamelCase      :			Tuple				):
   # Initialise PyTorch model
   __lowerCamelCase    				=     ReformerConfig.from_json_file(_UpperCamelCase				)
   print(F"""Building PyTorch model from configuration: {config}"""				)
   __lowerCamelCase    				=     ReformerModelWithLMHead(_UpperCamelCase				)

   with open(_UpperCamelCase			,'''rb'''				) as f:
      __lowerCamelCase    				=     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__":
  a_								=       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."""
  )
  a_								=       parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











import os
import sys
import unittest


a_								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_								=       os.path.join(git_repo_path, """src""", """transformers""")

a_								=       """
{0} = None
"""

a_								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


a_								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
    self.assertIsNone(__UpperCAmelCase					)

    __lowerCamelCase    				=     find_backend('''    if not is_tokenizers_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tokenizers'''					)

    __lowerCamelCase    				=     find_backend('''    if not is_tensorflow_text_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers_and_vision'''					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     read_init()
    # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
    self.assertIn('''torch'''						,					__UpperCAmelCase					)
    self.assertIn('''tensorflow_text'''						,					__UpperCAmelCase					)
    self.assertIn('''sentencepiece_and_tokenizers'''						,					__UpperCAmelCase					)

    # Likewise, we can't assert on the exact content of a key
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertModel'''						,					objects['''tf''']					)
    self.assertIn('''FlaxBertModel'''						,					objects['''flax''']					)
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertTokenizer'''						,					objects['''tensorflow_text''']					)
    self.assertIn('''convert_slow_tokenizer'''						,					objects['''sentencepiece_and_tokenizers''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     create_dummy_object('''CONSTANT'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					'''\nCONSTANT = None\n'''					)

    __lowerCamelCase    				=     create_dummy_object('''function'''						,					'''\'torch\''''					)
    self.assertEqual(
        __UpperCAmelCase						,					'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

    __lowerCamelCase    				=     '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
    __lowerCamelCase    				=     create_dummy_object('''FakeClass'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
    __lowerCamelCase    				=     create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
    self.assertEqual(dummy_files['''torch''']						,					__UpperCAmelCase					)






