


"""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 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Optional[Any]=None				)			->		Any:


    """simple docstring"""





    assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
    snake_case					:   int         =					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					:   Tuple         =					nn.Parameter(__lowerCAmelCase				)







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


    """simple docstring"""





    snake_case					:   Optional[int]         =					np.asarray(weights[0]				)
    snake_case					:   List[str]         =					np.asarray(weights[1]				)
    snake_case					:   List[str]         =					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		(		lowercase						:						Tuple		,							lowercase						:						List[str]		,							lowercase						:						Tuple				)			->		str:


    """simple docstring"""





    snake_case					:   List[Any]         =					np.asarray(weights[0]				)
    snake_case					:   Union[str, Any]         =					np.asarray(weights[1]				)
    snake_case					:   Optional[Any]         =					np.asarray(weights[2]				)
    snake_case					:   List[Any]         =					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		(		lowercase						:						List[str]		,							lowercase						:						str		,							lowercase						:						Optional[Any]				)			->		int:


    """simple docstring"""





    snake_case					:   List[str]         =					weights[0][0][0]
    snake_case					:   str         =					np.asarray(layer_norm_a[0]				)
    snake_case					:   List[Any]         =					np.asarray(layer_norm_a[1]				)
    set_param(
        torch_block.attention.layer_norm		,							torch.tensor(__lowerCAmelCase				)		,							torch.tensor(__lowerCAmelCase				)		,							)

    # lsh weights + output
    snake_case					:   Union[str, Any]         =					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					:   int         =					weights[2][0][1][2]

    # Chunked Feed Forward
    if len(__lowerCAmelCase				) == 4:
        snake_case					:   int         =					intermediate_weights[2]

    # layernorm 2
    snake_case					:   List[Any]         =					np.asarray(intermediate_weights[0][0]				)
    snake_case					:   List[Any]         =					np.asarray(intermediate_weights[0][1]				)
    set_param(
        torch_block.feed_forward.layer_norm		,							torch.tensor(__lowerCAmelCase				)		,							torch.tensor(__lowerCAmelCase				)		,							)

    # intermediate dense
    snake_case					:   Tuple         =					np.asarray(intermediate_weights[1][0]				)
    snake_case					:   int         =					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					:   str         =					np.asarray(intermediate_weights[4][0]				)
    snake_case					:   Union[str, Any]         =					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		(		lowercase						:						List[str]		,							lowercase						:						Optional[Any]		,							lowercase						:						Dict				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					torch_model.reformer

    # word embeds
    snake_case					:   int         =					np.asarray(weights[1]				)
    set_param(
        torch_model_reformer.embeddings.word_embeddings		,							torch.tensor(__lowerCAmelCase				)		,							)

    if isinstance(weights[3]		,							__lowerCAmelCase				):
        snake_case					:   List[Any]         =					torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights				)				):
            snake_case					:   int         =					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					:   Tuple         =					nn.Parameter(torch.tensor(__lowerCAmelCase				)				)

    snake_case					:   str         =					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					:   Union[str, Any]         =					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase				)

    # output layer norm
    snake_case					:   Union[str, Any]         =					np.asarray(weights[7][0]				)
    snake_case					:   Tuple         =					np.asarray(weights[7][1]				)
    set_param(
        torch_model_reformer.encoder.layer_norm		,							torch.tensor(__lowerCAmelCase				)		,							torch.tensor(__lowerCAmelCase				)		,							)

    # output embeddings
    snake_case					:   Any         =					np.asarray(weights[9][0]				)
    snake_case					:   Dict         =					np.asarray(weights[9][1]				)
    set_param(
        torch_model.lm_head.decoder		,							torch.tensor(__lowerCAmelCase				).transpose(0		,							1				).contiguous()		,							torch.tensor(__lowerCAmelCase				)		,							)







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						Dict		,							lowercase						:						List[str]				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					ReformerConfig.from_json_file(__lowerCAmelCase				)
    print(F'Building PyTorch model from configuration: {config}'				)
    snake_case					:   Optional[int]         =					ReformerModelWithLMHead(__lowerCAmelCase				)

    with open(__lowerCAmelCase		,							"rb"				) as f:
        snake_case					:   Optional[Any]         =					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__":
   __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)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					UNetaDModel(
           block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=("DownBlock2D", "AttnDownBlock2D")		,			up_block_types=("AttnUpBlock2D", "UpBlock2D")		,			)
       return model







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2





