





'''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None   )    ->       int:

   '''simple docstring'''







   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[2]   )
   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							weights[0][0][0]
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[1]   )
   set_param(
       torch_block.attention.layer_norm				,						torch.tensor(__UpperCAmelCase   )				,						torch.tensor(__UpperCAmelCase   )				,						)

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

   # Chunked Feed Forward
   if len(__UpperCAmelCase   ) == 4:
      __SCREAMING_SNAKE_CASE   				=							intermediate_weights[2]

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

   # intermediate dense
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[1][0]   )
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[4][0]   )
   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch_model.reformer

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

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

   __SCREAMING_SNAKE_CASE   				=							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   ):
      __SCREAMING_SNAKE_CASE   				=							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ReformerConfig.from_json_file(__UpperCAmelCase   )
   print(f"""Building PyTorch model from configuration: {config}"""   )
   __SCREAMING_SNAKE_CASE   				=							ReformerModelWithLMHead(__UpperCAmelCase   )

   with open(__UpperCAmelCase				,						"""rb"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							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)









"""simple docstring"""






UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""prompt""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset([])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""image"""])

UpperCamelCase__				:str			   =	frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""image"""])

UpperCamelCase__				:Optional[int]			   =	frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:int			   =	frozenset(["""prompt""", """image""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

UpperCamelCase__				:Dict			   =	frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Dict			   =	frozenset(["""image""", """mask_image"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""example_image""", """image""", """mask_image"""])

UpperCamelCase__				:List[str]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Any			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[Any]			   =	frozenset([])

UpperCamelCase__				:Optional[Any]			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[int]			   =	frozenset([])

UpperCamelCase__				:str			   =	frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""prompt""", """negative_prompt"""])
UpperCamelCase__				:Tuple			   =	frozenset(["""input_tokens"""])

UpperCamelCase__				:int			   =	frozenset(["""input_tokens"""])

