




'''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 						snake_case_       (UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Union[str, Any]=None ):





							'''simple docstring'''


							assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
							_a      	=							nn.Parameter(UpperCamelCase )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
														_a      	=							nn.Parameter(UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							np.asarray(weights[0] )
							_a      	=							np.asarray(weights[1] )
							_a      	=							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 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							np.asarray(weights[0] )
							_a      	=							np.asarray(weights[1] )
							_a      	=							np.asarray(weights[2] )
							_a      	=							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 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    int ):





							'''simple docstring'''


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

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

							# Chunked Feed Forward
							if len(UpperCamelCase ) == 4:
														_a      	=							intermediate_weights[2]

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

							# intermediate dense
							_a      	=							np.asarray(intermediate_weights[1][0] )
							_a      	=							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
							_a      	=							np.asarray(intermediate_weights[4][0] )
							_a      	=							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 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    str					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							torch_model.reformer

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

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

							_a      	=							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 ):
														_a      	=							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )

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

							# output embeddings
							_a      	=							np.asarray(weights[9][0] )
							_a      	=							np.asarray(weights[9][1] )
							set_param(
							    torch_model.lm_head.decoder					,		torch.tensor(UpperCamelCase ).transpose(0					,		1 ).contiguous()					,		torch.tensor(UpperCamelCase )					,		)
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							_a      	=							ReformerConfig.from_json_file(UpperCamelCase )
							print(f'Building PyTorch model from configuration: {config}' )
							_a      	=							ReformerModelWithLMHead(UpperCamelCase )

							with open(UpperCamelCase					,		'''rb''' ) as f:
														_a      	=							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__":
				_snake_case : 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.'
				)
				_snake_case : Tuple  				=						parser.parse_args()
				convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =KandinskyImgaImgPipeline
       UpperCAmelCase		 =["prompt", "image_embeds", "negative_image_embeds", "image"]
       UpperCAmelCase		 =[
           "prompt",
           "negative_prompt",
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       UpperCAmelCase		 =[
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "negative_prompt",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase		 =False






       @property
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          return 3_2






       @property
       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          return 3_2






       @property
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          return self.time_input_dim






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          return self.time_input_dim * 4






       @property
       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          return 1_0_0






       @property
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
          return tokenizer






       @property
       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase  :  Optional[Any]				       =MCLIPConfig(
              numDims=self.cross_attention_dim    ,				transformerDimensions=self.text_embedder_hidden_size    ,				hidden_size=self.text_embedder_hidden_size    ,				intermediate_size=3_7    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				vocab_size=1_0_0_5    ,				)

          _UpperCAmelCase  :  List[Any]				       =MultilingualCLIP(snake_case)
          _UpperCAmelCase  :  List[Any]				       =text_encoder.eval()

          return text_encoder






       @property
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          torch.manual_seed(0)

          _UpperCAmelCase  :  Union[str, Any]				       ={
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          _UpperCAmelCase  :  Tuple				       =UNetaDConditionModel(**snake_case)
          return model






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          return {
              "block_out_channels": [3_2, 6_4],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 1_2,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }






       @property
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase  :  List[Any]				       =VQModel(**self.dummy_movq_kwargs)
          return model






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =self.dummy_text_encoder
          _UpperCAmelCase  :  Any				       =self.dummy_tokenizer
          _UpperCAmelCase  :  List[str]				       =self.dummy_unet
          _UpperCAmelCase  :  Union[str, Any]				       =self.dummy_movq

          _UpperCAmelCase  :  List[str]				       ={
              'num_train_timesteps': 1_0_0_0,
              'beta_schedule': 'linear',
              'beta_start': 0.0_00_85,
              'beta_end': 0.0_12,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          _UpperCAmelCase  :  Dict				       =DDIMScheduler(**snake_case)

          _UpperCAmelCase  :  Optional[int]				       ={
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=0)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(snake_case)).to(snake_case)
          _UpperCAmelCase  :  Tuple				       =floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(seed + 1)).to(snake_case)
          # create init_image
          _UpperCAmelCase  :  Dict				       =floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(snake_case)).to(snake_case)
          _UpperCAmelCase  :  Optional[int]				       =image.cpu().permute(0    ,				2    ,				3    ,				1)[0]
          _UpperCAmelCase  :  List[Any]				       =Image.fromarray(np.uinta(snake_case)).convert('RGB').resize((2_5_6, 2_5_6))

          if str(snake_case).startswith('mps'):
             _UpperCAmelCase  :  str				       =torch.manual_seed(snake_case)
          else:
             _UpperCAmelCase  :  Optional[Any]				       =torch.Generator(device=snake_case).manual_seed(snake_case)
          _UpperCAmelCase  :  str				       ={
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 6_4,
              'width': 6_4,
              'num_inference_steps': 1_0,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       ='cpu'

          _UpperCAmelCase  :  Dict				       =self.get_dummy_components()

          _UpperCAmelCase  :  Optional[Any]				       =self.pipeline_class(**snake_case)
          _UpperCAmelCase  :  List[str]				       =pipe.to(snake_case)

          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase  :  List[Any]				       =pipe(**self.get_dummy_inputs(snake_case))
          _UpperCAmelCase  :  List[str]				       =output.images

          _UpperCAmelCase  :  Any				       =pipe(
              **self.get_dummy_inputs(snake_case)    ,				return_dict=snake_case    ,				)[0]

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

          assert image.shape == (1, 6_4, 6_4, 3)

          _UpperCAmelCase  :  List[Any]				       =np.array(
              [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33])
          assert (
              np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
          ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
          ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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






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




          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy')

          _UpperCAmelCase  :  List[Any]				       =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
          _UpperCAmelCase  :  Union[str, Any]				       ='A red cartoon frog, 4k'

          _UpperCAmelCase  :  Union[str, Any]				       =KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'    ,				torch_dtype=torch.floataa)
          pipe_prior.to(snake_case)

          _UpperCAmelCase  :  Optional[Any]				       =KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'    ,				torch_dtype=torch.floataa)
          _UpperCAmelCase  :  Tuple				       =pipeline.to(snake_case)

          pipeline.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase  :  List[str]				       =torch.Generator(device='cpu').manual_seed(0)
          _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =pipe_prior(
              snake_case    ,				generator=snake_case    ,				num_inference_steps=5    ,				negative_prompt=''    ,				).to_tuple()

          _UpperCAmelCase  :  str				       =pipeline(
              snake_case    ,				image=snake_case    ,				image_embeds=snake_case    ,				negative_image_embeds=snake_case    ,				generator=snake_case    ,				num_inference_steps=1_0_0    ,				height=7_6_8    ,				width=7_6_8    ,				strength=0.2    ,				output_type='np'    ,				)

          _UpperCAmelCase  :  Any				       =output.images[0]

          assert image.shape == (7_6_8, 7_6_8, 3)

          assert_mean_pixel_difference(snake_case    ,				snake_case)

