
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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        SCREAMING_SNAKE_CASE__					(     snake_case_							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		str          = KandinskyVaaPipeline
	_UpperCAmelCase	:		str          = [
	    "image_embeds",
	    "negative_image_embeds",
	]
	_UpperCAmelCase	:		str          = ["image_embeds", "negative_image_embeds"]
	_UpperCAmelCase	:		int          = [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	_UpperCAmelCase	:		Dict          = False



	@property
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				return self.time_input_dim



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				return self.time_input_dim * 4



	@property
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				return 100



	@property
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				torch.manual_seed(0     )

				_snake_case          =      {
				    'in_channels': 4,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': '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': 'image_proj',
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': None,
				}

				_snake_case          =      UNetaDConditionModel(**_A     )
				return model



	@property
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				return {
				    "block_out_channels": [32, 64],
				    "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": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      VQModel(**self.dummy_movq_kwargs     )
				return model



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_unet
				_snake_case          =      self.dummy_movq

				_snake_case          =      DDIMScheduler(
				    num_train_timesteps=1_000      ,  beta_schedule='linear'      ,  beta_start=0.00085      ,  beta_end=0.012      ,  clip_sample=_A      ,  set_alpha_to_one=_A      ,  steps_offset=1      ,  prediction_type='epsilon'      ,  thresholding=_A      ,  )

				_snake_case          =      {
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}
				return components



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Tuple      ,  lowercase     :   List[str]=0     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor((1, self.text_embedder_hidden_size)      ,  rng=random.Random(_A     )     ).to(_A     )
				_snake_case          =      floats_tensor((1, self.text_embedder_hidden_size)      ,  rng=random.Random(seed + 1     )     ).to(
				    _A     )
				if str(_A     ).startswith('mps'     ):
							_snake_case          =      torch.manual_seed(_A     )
				else:
							_snake_case          =      torch.Generator(device=_A     ).manual_seed(_A     )
				_snake_case          =      {
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'guidance_scale': 4.0,
				    'num_inference_steps': 2,
				    'output_type': 'np',
				}
				return inputs





	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.get_dummy_components()

				_snake_case          =      self.pipeline_class(**_A     )
				_snake_case          =      pipe.to(_A     )

				pipe.set_progress_bar_config(disable=_A     )

				_snake_case          =      pipe(**self.get_dummy_inputs(_A     )     )
				_snake_case          =      output.images

				_snake_case          =      pipe(
				    **self.get_dummy_inputs(_A     )      ,  return_dict=_A      ,  )[0]

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

				assert image.shape == (1, 64, 64, 3)

				_snake_case          =      np.array(
				    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]     )

				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        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'     )

				_snake_case          =      KandinskyVaaPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'      ,  torch_dtype=torch.floataa     )
				pipe_prior.to(_A     )

				_snake_case          =      KandinskyVaaPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-decoder'      ,  torch_dtype=torch.floataa     )
				_snake_case          =      pipeline.to(_A     )
				pipeline.set_progress_bar_config(disable=_A     )

				_snake_case          =      'red cat, 4k photo'

				_snake_case          =      torch.Generator(device='cuda'     ).manual_seed(0     )
				_snake_case          =      pipe_prior(
				    _A      ,  generator=_A      ,  num_inference_steps=5      ,  negative_prompt=''      ,  ).to_tuple()

				_snake_case          =      torch.Generator(device='cuda'     ).manual_seed(0     )
				_snake_case          =      pipeline(
				    image_embeds=_A      ,  negative_image_embeds=_A      ,  generator=_A      ,  num_inference_steps=100      ,  output_type='np'      ,  )

				_snake_case          =      output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(_A      ,  _A     )

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)