"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCAmelCase    :							Any													=						False

class       a_      (     unittest.TestCase			):
					pass

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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )

							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt="""first prompt"""  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type="""numpy"""  ,						).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained(snake_case__  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   generator.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt="""first prompt"""  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type="""numpy"""  ,						).images

							assert np.abs(image - new_image     ).sum() < 1E-5, "Models don't have the same forward pass"




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   """cyberpunk 2077"""
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.dual_guided(
							    prompt=snake_case__  ,						image=snake_case__  ,						text_to_image_strength=0.75  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type="""numpy"""  ,						).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]     )

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

							lowerCAmelCase__        =   """A painting of a squirrel eating a burger """
							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe.text_to_image(
							    prompt=snake_case__  ,						generator=snake_case__  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type="""numpy"""     ).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]     )

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

							lowerCAmelCase__        =   pipe.image_variation(snake_case__  ,						generator=snake_case__  ,						output_type="""numpy"""     ).images

							lowerCAmelCase__        =   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__        =   np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]     )

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







"""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)




