

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class   _a					(			unittest.TestCase				):




      """simple docstring"""

      @property
      def 		__A	(   self							:				List[Any]	):
            torch.manual_seed(0	)
            A_						  =				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





      @property
      def 		__A	(   self							:				int	):
            torch.manual_seed(0	)
            A_						  =				VQModel(
                block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,			latent_channels=3						,			)
            return model





      @property
      def 		__A	(   self							:				Optional[Any]	):
            torch.manual_seed(0	)
            A_						  =				CLIPTextConfig(
                bos_token_id=0						,			eos_token_id=2						,			hidden_size=32						,			intermediate_size=37						,			layer_norm_eps=1E-05						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=1000						,			)
            return CLIPTextModel(UpperCAmelCase	)







      def 		__A	(   self							:				Optional[int]	):
            A_						  =				self.dummy_uncond_unet
            A_						  =				DDIMScheduler()
            A_						  =				self.dummy_vq_model

            A_						  =				LDMPipeline(unet=UpperCAmelCase						,			vqvae=UpperCAmelCase						,			scheduler=UpperCAmelCase	)
            ldm.to(UpperCAmelCase	)
            ldm.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=2						,			output_type="numpy"	).images

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=2						,			output_type="numpy"						,			return_dict=UpperCAmelCase	)[0]

            A_						  =				image[0, -3:, -3:, -1]
            A_						  =				image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            A_						  =				np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]	)
            A_						  =				1E-2 if torch_device != "mps" else 3E-2

            assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




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




      """simple docstring"""

      def 		__A	(   self							:				Optional[int]	):
            A_						  =				LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
            ldm.to(UpperCAmelCase	)
            ldm.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=5						,			output_type="numpy"	).images

            A_						  =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)
            A_						  =				np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]	)
            A_						  =				1E-2 if torch_device != "mps" else 3E-2

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






'''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			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	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'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

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

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

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

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	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
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

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

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

	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							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

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

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		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__					:			Optional[int]          =     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__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



