


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    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:Union[str, Any]=None				)->Dict:
   '''simple docstring'''






   assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
   snake_case_            =       nn.Parameter(lowerCAmelCase_				)
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
      snake_case_            =       nn.Parameter(lowerCAmelCase_				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Optional[int]				)->Optional[int]:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:int				)->Optional[Any]:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   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(lowerCAmelCase_				)		,    torch.tensor(lowerCAmelCase_				)		,    )

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

   # intermediate weighs
   snake_case_            =       weights[2][0][1][2]

   # Chunked Feed Forward
   if len(lowerCAmelCase_				) == 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(lowerCAmelCase_				)		,    torch.tensor(lowerCAmelCase_				)		,    )

   # 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(lowerCAmelCase_				).transpose(0		,    1				).contiguous()		,    torch.tensor(lowerCAmelCase_				)		,    )

   # 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(lowerCAmelCase_				).transpose(0		,    1				).contiguous()		,    torch.tensor(lowerCAmelCase_				)		,    )



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:List[Any]				)->Union[str, Any]:
   '''simple docstring'''






   snake_case_            =       torch_model.reformer

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

   if isinstance(weights[3]		,    lowerCAmelCase_				):
      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(lowerCAmelCase_				)				)

   snake_case_            =       weights[5]
   assert len(torch_model_reformer.encoder.layers				) * 4 == len(
       lowerCAmelCase_				), "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(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

   # 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(lowerCAmelCase_				)		,    torch.tensor(lowerCAmelCase_				)		,    )

   # 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(lowerCAmelCase_				).transpose(0		,    1				).contiguous()		,    torch.tensor(lowerCAmelCase_				)		,    )



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Tuple				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       ReformerConfig.from_json_file(lowerCAmelCase_				)
   print(F'''Building PyTorch model from configuration: {config}'''				)
   snake_case_            =       ReformerModelWithLMHead(lowerCAmelCase_				)

   with open(lowerCAmelCase_		,    "rb"				) as f:
      snake_case_            =       pickle.load(lowerCAmelCase_				)["weights"]

   set_model_weights_in_torch(lowerCAmelCase_		,    lowerCAmelCase_		,    config.hidden_size				)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:List[str]			=  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.'''
   )
   SCREAMING_SNAKE_CASE						:Tuple			=  parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						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_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

