
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__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None						)  ->	Tuple:
    # set parameter of one layer
    assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
    UpperCamelCase_:							Dict	=						nn.Parameter(UpperCamelCase__						)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
        UpperCamelCase_:							Any	=						nn.Parameter(UpperCamelCase__						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
    # set torch weights for 1-to-1 comparison
    UpperCamelCase_:							Dict	=						np.asarray(weights[0]						)
    UpperCamelCase_:							List[Any]	=						np.asarray(weights[1]						)
    UpperCamelCase_:							List[str]	=						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__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
    # set torch weights for 1-to-1 comparison
    UpperCamelCase_:							Tuple	=						np.asarray(weights[0]						)
    UpperCamelCase_:							Any	=						np.asarray(weights[1]						)
    UpperCamelCase_:							List[Any]	=						np.asarray(weights[2]						)
    UpperCamelCase_:							List[str]	=						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__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
    # layernorm 1
    UpperCamelCase_:							str	=						weights[0][0][0]
    UpperCamelCase_:							Optional[int]	=						np.asarray(layer_norm_a[0]						)
    UpperCamelCase_:							Tuple	=						np.asarray(layer_norm_a[1]						)
    set_param(
        torch_block.attention.layer_norm     ,    torch.tensor(UpperCamelCase__						)     ,    torch.tensor(UpperCamelCase__						)     ,    )

    # lsh weights + output
    UpperCamelCase_:							List[Any]	=						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
    UpperCamelCase_:							int	=						weights[2][0][1][2]

    # Chunked Feed Forward
    if len(UpperCamelCase__						) == 4:
        UpperCamelCase_:							Dict	=						intermediate_weights[2]

    # layernorm 2
    UpperCamelCase_:							Optional[int]	=						np.asarray(intermediate_weights[0][0]						)
    UpperCamelCase_:							Tuple	=						np.asarray(intermediate_weights[0][1]						)
    set_param(
        torch_block.feed_forward.layer_norm     ,    torch.tensor(UpperCamelCase__						)     ,    torch.tensor(UpperCamelCase__						)     ,    )

    # intermediate dense
    UpperCamelCase_:							Optional[Any]	=						np.asarray(intermediate_weights[1][0]						)
    UpperCamelCase_:							Union[str, Any]	=						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
    UpperCamelCase_:							Optional[int]	=						np.asarray(intermediate_weights[4][0]						)
    UpperCamelCase_:							List[Any]	=						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__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
    # reformer model
    UpperCamelCase_:							List[Any]	=						torch_model.reformer

    # word embeds
    UpperCamelCase_:							Union[str, Any]	=						np.asarray(weights[1]						)
    set_param(
        torch_model_reformer.embeddings.word_embeddings     ,    torch.tensor(UpperCamelCase__						)     ,    )

    if isinstance(weights[3]     ,    UpperCamelCase__						):
        UpperCamelCase_:							Union[str, Any]	=						torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights						)						):
            UpperCamelCase_:							str	=						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'''
            UpperCamelCase_:							Dict	=						nn.Parameter(torch.tensor(UpperCamelCase__						)						)

    UpperCamelCase_:							int	=						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						):
        UpperCamelCase_:							Dict	=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__						)

    # output layer norm
    UpperCamelCase_:							Any	=						np.asarray(weights[7][0]						)
    UpperCamelCase_:							List[str]	=						np.asarray(weights[7][1]						)
    set_param(
        torch_model_reformer.encoder.layer_norm     ,    torch.tensor(UpperCamelCase__						)     ,    torch.tensor(UpperCamelCase__						)     ,    )

    # output embeddings
    UpperCamelCase_:							List[Any]	=						np.asarray(weights[9][0]						)
    UpperCamelCase_:							Optional[int]	=						np.asarray(weights[9][1]						)
    set_param(
        torch_model.lm_head.decoder     ,    torch.tensor(UpperCamelCase__						).transpose(0     ,    1						).contiguous()     ,    torch.tensor(UpperCamelCase__						)     ,    )







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
    # Initialise PyTorch model
    UpperCamelCase_:							Union[str, Any]	=						ReformerConfig.from_json_file(UpperCamelCase__						)
    print(F'''Building PyTorch model from configuration: {config}'''						)
    UpperCamelCase_:							str	=						ReformerModelWithLMHead(UpperCamelCase__						)

    with open(UpperCamelCase__     ,    """rb"""						) as f:
        UpperCamelCase_:							str	=						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__":
       lowerCamelCase_						: Dict         =  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."""
       )
       lowerCamelCase_						: Optional[int]         =  parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








from manim import *

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						Rectangle(height=0.5						,	width=0.5      )
							UpperCamelCase_:							Dict	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0      )

							UpperCamelCase_:							Tuple	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Tuple	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Union[str, Any]	=						VGroup(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[Any]	=						Text("""CPU"""						,	font_size=24      )
							UpperCamelCase_:							int	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							cpu.move_to([-2.5, -0.5, 0]      )
							self.add(snake_case_      )

							UpperCamelCase_:							Optional[int]	=						[mem.copy() for i in range(1      )]
							UpperCamelCase_:							Dict	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[int]	=						Text("""GPU"""						,	font_size=24      )
							UpperCamelCase_:							Optional[int]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							gpu.align_to(snake_case_						,	snake_case_      )
							gpu.set_x(gpu.get_x() - 1      )

							self.add(snake_case_      )

							UpperCamelCase_:							Dict	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )

							UpperCamelCase_:							Any	=						Text("""Model"""						,	font_size=24      )
							UpperCamelCase_:							Optional[Any]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							model.move_to([3, -1.0, 0]      )

							self.play(
							    Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	)

							UpperCamelCase_:							List[Any]	=						MarkupText(
							    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''						,	font_size=24						,	)

							UpperCamelCase_:							Optional[Any]	=						Square(side_length=2.2      )
							key.move_to([-5, 2, 0]      )

							UpperCamelCase_:							Union[str, Any]	=						MarkupText(
							    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''						,	font_size=18						,	)

							key_text.move_to([-5, 2.4, 0]      )

							step_a.move_to([2, 2, 0]      )
							self.play(Write(snake_case_						,	run_time=2.5      )						,	Write(snake_case_      )						,	Write(snake_case_      )      )

							self.add(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Tuple	=						[]
							for i, rect in enumerate(snake_case_      ):
											UpperCamelCase_:							Tuple	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0.0      ).set_fill(snake_case_						,	opacity=0.7      )
											cpu_target.move_to(snake_case_      )
											cpu_target.generate_target()
											UpperCamelCase_:							int	=						0.46 / 4
											UpperCamelCase_:							Optional[int]	=						0.46 / 3

											if i == 0:
															cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )						,	buff=0.02						,	direction=snake_case_      )
															cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
											elif i == 3:
															cpu_target.target.next_to(cpu_targs[0].target						,	direction=snake_case_						,	buff=0.0      )
											else:
															cpu_target.target.next_to(cpu_targs[i - 1].target						,	direction=snake_case_						,	buff=0.0      )
											cpu_targs.append(snake_case_      )

											first_animations.append(rect.animate(run_time=0.5      ).set_stroke(snake_case_      )      )
											second_animations.append(MoveToTarget(snake_case_						,	run_time=1.5      )      )

							self.play(*snake_case_      )
							self.play(*snake_case_      )

							self.wait()






