






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_       (					_A					,      _A					,      _A=None     )	->	List[str]:







				"""simple docstring"""
				assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
				snake_case__													=   nn.Parameter(__a     )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
								snake_case__													=   nn.Parameter(__a     )
def    a_       (					_A					,      _A					,      _A     )	->	List[str]:







				"""simple docstring"""
				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(__a     ).transpose(1					,      2     ).contiguous().view(-1					,      __a     )					,      )
				set_param(
				    torch_layer.self_attention.value					,      torch.tensor(__a     ).transpose(1					,      2     ).contiguous().view(-1					,      __a     )					,      )
				set_param(
				    torch_layer.output.dense					,      torch.tensor(__a     ).view(-1					,      __a     ).contiguous().transpose(0					,      1     )					,      )
def    a_       (					_A					,      _A					,      _A     )	->	str:







				"""simple docstring"""
				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(__a     ).transpose(1					,      2     ).contiguous().view(-1					,      __a     )					,      )
				set_param(
				    torch_layer.self_attention.key					,      torch.tensor(__a     ).transpose(1					,      2     ).contiguous().view(-1					,      __a     )					,      )
				set_param(
				    torch_layer.self_attention.value					,      torch.tensor(__a     ).transpose(1					,      2     ).contiguous().view(-1					,      __a     )					,      )
				set_param(
				    torch_layer.output.dense					,      torch.tensor(__a     ).view(-1					,      __a     ).contiguous().transpose(0					,      1     )					,      )
def    a_       (					_A					,      _A					,      _A     )	->	Optional[Any]:







				"""simple docstring"""
				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(__a     )					,      torch.tensor(__a     )					,      )

				# lsh weights + output
				snake_case__													=   weights[0][1]
				if len(__a     ) < 4:
								set_layer_weights_in_torch_lsh(__a					,      torch_block.attention					,      __a     )
				else:
								set_layer_weights_in_torch_local(__a					,      torch_block.attention					,      __a     )

				# intermediate weighs
				snake_case__													=   weights[2][0][1][2]

				# Chunked Feed Forward
				if len(__a     ) == 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(__a     )					,      torch.tensor(__a     )					,      )

				# 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(__a     ).transpose(0					,      1     ).contiguous()					,      torch.tensor(__a     )					,      )

				# 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(__a     ).transpose(0					,      1     ).contiguous()					,      torch.tensor(__a     )					,      )
def    a_       (					_A					,      _A					,      _A     )	->	int:







				"""simple docstring"""
				snake_case__													=   torch_model.reformer

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

				if isinstance(weights[3]					,      __a     ):
								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(__a     )     )

				snake_case__													=   weights[5]
				assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
				    __a     ), "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(__a					,      __a					,      __a     )

				# 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(__a     )					,      torch.tensor(__a     )					,      )

				# 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(__a     ).transpose(0					,      1     ).contiguous()					,      torch.tensor(__a     )					,      )
def    a_       (					_A					,      _A					,      _A     )	->	List[Any]:







				"""simple docstring"""
				snake_case__													=   ReformerConfig.from_json_file(__a     )
				print(f'''Building PyTorch model from configuration: {config}'''     )
				snake_case__													=   ReformerModelWithLMHead(__a     )

				with open(__a					,      'rb'     ) as f:
								snake_case__													=   pickle.load(__a     )['weights']

				set_model_weights_in_torch(__a					,      __a					,      config.hidden_size     )

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''     )
				torch.save(model.state_dict()					,      __a     )


if __name__ == "__main__":
							__UpperCamelCase    :						Optional[Any]		     =  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."""
							)
							__UpperCamelCase    :						List[Any]		     =  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 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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   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   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   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   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   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(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

     _a				:	str			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

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



