"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   UNetaDModel(
							    sample_size=(32, 64)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,						up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,						)
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   UNetaDConditionModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")  ,						up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")  ,						cross_attention_dim=10  ,						)
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   AutoencoderKL(
							    sample_size=(128, 64)  ,						in_channels=1  ,						out_channels=1  ,						latent_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")  ,						up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")  ,						)
							lowerCAmelCase__        =   UNetaDModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,						up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,						)
							return vqvae, unet




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							lowerCAmelCase__        =   DDPMScheduler()
							lowerCAmelCase__        =   AudioDiffusionPipeline(vqvae=snake_case__  ,						unet=self.dummy_unet  ,						mel=snake_case__  ,						scheduler=snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(42     )
							lowerCAmelCase__        =   pipe(generator=snake_case__  ,						steps=4     )
							lowerCAmelCase__        =   output.audios[0]
							lowerCAmelCase__        =   output.images[0]

							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(42     )
							lowerCAmelCase__        =   pipe(generator=snake_case__  ,						steps=4  ,						return_dict=snake_case__     )
							lowerCAmelCase__        =   output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							lowerCAmelCase__        =   np.frombuffer(image.tobytes()  ,						dtype="""uint8"""     )[:10]
							lowerCAmelCase__        =   np.frombuffer(image_from_tuple.tobytes()  ,						dtype="""uint8"""     )[:10]
							lowerCAmelCase__        =   np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() == 0

							lowerCAmelCase__        =   Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,						)

							lowerCAmelCase__        =   DDIMScheduler()
							lowerCAmelCase__        =   self.dummy_vqvae_and_unet
							lowerCAmelCase__        =   AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=snake_case__  ,						scheduler=snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							np.random.seed(0     )
							lowerCAmelCase__        =   np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)     )
							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(42     )
							lowerCAmelCase__        =   pipe(raw_audio=snake_case__  ,						generator=snake_case__  ,						start_step=5  ,						steps=10     )
							lowerCAmelCase__        =   output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							lowerCAmelCase__        =   np.frombuffer(image.tobytes()  ,						dtype="""uint8"""     )[:10]
							lowerCAmelCase__        =   np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0

							lowerCAmelCase__        =   self.dummy_unet_condition
							lowerCAmelCase__        =   AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=snake_case__  ,						mel=snake_case__  ,						scheduler=snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							np.random.seed(0     )
							lowerCAmelCase__        =   torch.rand((1, 1, 10)     )
							lowerCAmelCase__        =   pipe(generator=snake_case__  ,						encoding=snake_case__     )
							lowerCAmelCase__        =   output.images[0]
							lowerCAmelCase__        =   np.frombuffer(image.tobytes()  ,						dtype="""uint8"""     )[:10]
							lowerCAmelCase__        =   np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0

@slow
@require_torch_gpu
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   torch_device

							lowerCAmelCase__        =   DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(42     )
							lowerCAmelCase__        =   pipe(generator=snake_case__     )
							lowerCAmelCase__        =   output.audios[0]
							lowerCAmelCase__        =   output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							lowerCAmelCase__        =   np.frombuffer(image.tobytes()  ,						dtype="""uint8"""     )[:10]
							lowerCAmelCase__        =   np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0







"""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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

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

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

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

		# intermediate dense
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][0]     )
		lowerCAmelCase__        =   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
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

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

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

		lowerCAmelCase__        =   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     ):
				lowerCAmelCase__        =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   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__":
			__lowerCAmelCase    :							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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




