





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 lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  Dict       ,      snake_case_	:  Optional[int]=None       )      ->				List[str]:
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __snake_case							       =   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"""
      __snake_case							       =   nn.Parameter(SCREAMING_SNAKE_CASE__       )






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Any       )      ->				int:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   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 lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  int       ,      snake_case_	:  str       )      ->				Any:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   np.asarray(weights[2]       )
   __snake_case							       =   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 lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  List[str]       ,      snake_case_	:  Tuple       )      ->				Any:
   # layernorm 1
   __snake_case							       =   weights[0][0][0]
   __snake_case							       =   np.asarray(layer_norm_a[0]       )
   __snake_case							       =   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
   __snake_case							       =   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
   __snake_case							       =   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(SCREAMING_SNAKE_CASE__       ) == 4:
      __snake_case							       =   intermediate_weights[2]

   # layernorm 2
   __snake_case							       =   np.asarray(intermediate_weights[0][0]       )
   __snake_case							       =   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
   __snake_case							       =   np.asarray(intermediate_weights[1][0]       )
   __snake_case							       =   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
   __snake_case							       =   np.asarray(intermediate_weights[4][0]       )
   __snake_case							       =   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 lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Any       )      ->				List[str]:
   # reformer model
   __snake_case							       =   torch_model.reformer

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

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

   __snake_case							       =   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       ):
      __snake_case							       =   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
   __snake_case							       =   np.asarray(weights[7][0]       )
   __snake_case							       =   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
   __snake_case							       =   np.asarray(weights[9][0]       )
   __snake_case							       =   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 lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  int       ,      snake_case_	:  str       )      ->				List[Any]:
   # Initialise PyTorch model
   __snake_case							       =   ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
   print(f"""Building PyTorch model from configuration: {config}"""       )
   __snake_case							       =   ReformerModelWithLMHead(SCREAMING_SNAKE_CASE__       )

   with open(SCREAMING_SNAKE_CASE__       ,      '''rb'''       ) as f:
      __snake_case							       =   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__":
    snake_case_				=			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.'
    )
    snake_case_				=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)