


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 					__magic_name__		(			__lowerCamelCase			,						unittest.TestCase     ):
       __A			:      int        =    UnCLIPImageVariationPipeline
       __A			:      Optional[int]        =    IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
       __A			:      str        =    IMAGE_VARIATION_BATCH_PARAMS

       __A			:      str        =    [
           "generator",
           "return_dict",
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]
       __A			:      List[str]        =    False






       @property
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              return 3_2






       @property
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              return 3_2






       @property
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              return self.time_input_dim






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              return self.time_input_dim * 4






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

              '''simple docstring'''






              return 1_0_0






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Any       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
              return tokenizer






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

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Union[str, Any]       							=				CLIPTextConfig(
                  bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
              return CLIPTextModelWithProjection(UpperCAmelCase_				)






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Tuple       							=				CLIPVisionConfig(
                  hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     num_hidden_layers=5  ,     num_attention_heads=4  ,     image_size=3_2  ,     intermediate_size=3_7  ,     patch_size=1  ,     )
              return CLIPVisionModelWithProjection(UpperCAmelCase_				)






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              torch.manual_seed(0				)

              lowercase		:Dict       							=				{
                  'clip_embeddings_dim': self.text_embedder_hidden_size,
                  'time_embed_dim': self.time_embed_dim,
                  'cross_attention_dim': self.cross_attention_dim,
              }

              lowercase		:int       							=				UnCLIPTextProjModel(**UpperCAmelCase_				)
              return model






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

              '''simple docstring'''






              torch.manual_seed(0				)

              lowercase		:str       							=				{
                  'sample_size': 3_2,
                  # RGB in channels
                  'in_channels': 3,
                  # Out channels is double in channels because predicts mean and variance
                  'out_channels': 6,
                  '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,
                  'cross_attention_dim': self.cross_attention_dim,
                  'attention_head_dim': 4,
                  'resnet_time_scale_shift': 'scale_shift',
                  'class_embed_type': 'identity',
              }

              lowercase		:Optional[Any]       							=				UNetaDConditionModel(**UpperCAmelCase_				)
              return model






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

              '''simple docstring'''






              return {
                  "sample_size": 6_4,
                  "layers_per_block": 1,
                  "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                  "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                  "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                  "in_channels": 6,
                  "out_channels": 3,
              }






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

              '''simple docstring'''






              torch.manual_seed(0				)

              lowercase		:Any       							=				UNetaDModel(**self.dummy_super_res_kwargs				)
              return model






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

              '''simple docstring'''






              torch.manual_seed(1				)

              lowercase		:Any       							=				UNetaDModel(**self.dummy_super_res_kwargs				)
              return model






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				self.dummy_decoder
              lowercase		:Dict       							=				self.dummy_text_proj
              lowercase		:int       							=				self.dummy_text_encoder
              lowercase		:Tuple       							=				self.dummy_tokenizer
              lowercase		:Optional[Any]       							=				self.dummy_super_res_first
              lowercase		:List[str]       							=				self.dummy_super_res_last

              lowercase		:Optional[Any]       							=				UnCLIPScheduler(
                  variance_type='''learned_range'''  ,     prediction_type='''epsilon'''  ,     num_train_timesteps=1_0_0_0  ,     )

              lowercase		:Tuple       							=				UnCLIPScheduler(
                  variance_type='''fixed_small_log'''  ,     prediction_type='''epsilon'''  ,     num_train_timesteps=1_0_0_0  ,     )

              lowercase		:List[Any]       							=				CLIPImageProcessor(crop_size=3_2  ,     size=3_2				)

              lowercase		:Optional[Any]       							=				self.dummy_image_encoder

              return {
                  "decoder": decoder,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "text_proj": text_proj,
                  "feature_extractor": feature_extractor,
                  "image_encoder": image_encoder,
                  "super_res_first": super_res_first,
                  "super_res_last": super_res_last,
                  "decoder_scheduler": decoder_scheduler,
                  "super_res_scheduler": super_res_scheduler,
              }






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Union[str, Any]=0  ,     snake_case__  :    Dict=True				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				floats_tensor((1, 3, 3_2, 3_2)  ,     rng=random.Random(UpperCAmelCase_				)				).to(UpperCAmelCase_				)
              if str(UpperCAmelCase_				).startswith('''mps'''				):
                     lowercase		:Optional[int]       							=				torch.manual_seed(UpperCAmelCase_				)
              else:
                     lowercase		:str       							=				torch.Generator(device=UpperCAmelCase_				).manual_seed(UpperCAmelCase_				)

              if pil_image:
                     lowercase		:List[Any]       							=				input_image * 0.5 + 0.5
                     lowercase		:str       							=				input_image.clamp(0  ,     1				)
                     lowercase		:Optional[Any]       							=				input_image.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()
                     lowercase		:int       							=				DiffusionPipeline.numpy_to_pil(UpperCAmelCase_				)[0]

              return {
                  "image": input_image,
                  "generator": generator,
                  "decoder_num_inference_steps": 2,
                  "super_res_num_inference_steps": 2,
                  "output_type": "np",
              }






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Any       							=				'cpu'

              lowercase		:Optional[Any]       							=				self.get_dummy_components()

              lowercase		:Dict       							=				self.pipeline_class(**UpperCAmelCase_				)
              lowercase		:int       							=				pipe.to(UpperCAmelCase_				)

              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              lowercase		:Optional[int]       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)

              lowercase		:Any       							=				pipe(**UpperCAmelCase_				)
              lowercase		:int       							=				output.images

              lowercase		:Dict       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)

              lowercase		:List[str]       							=				pipe(
                  **UpperCAmelCase_  ,     return_dict=UpperCAmelCase_  ,     )[0]

              lowercase		:int       							=				image[0, -3:, -3:, -1]
              lowercase		:Dict       							=				image_from_tuple[0, -3:, -3:, -1]

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

              lowercase		:int       							=				np.array(
                  [
                      0.99_97,
                      0.00_02,
                      0.99_97,
                      0.99_97,
                      0.99_69,
                      0.00_23,
                      0.99_97,
                      0.99_69,
                      0.99_70,
                  ]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
              assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Tuple       							=				'cpu'

              lowercase		:Dict       							=				self.get_dummy_components()

              lowercase		:Any       							=				self.pipeline_class(**UpperCAmelCase_				)
              lowercase		:Any       							=				pipe.to(UpperCAmelCase_				)

              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              lowercase		:List[str]       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)

              lowercase		:Union[str, Any]       							=				pipe(**UpperCAmelCase_				)
              lowercase		:Optional[Any]       							=				output.images

              lowercase		:Union[str, Any]       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)

              lowercase		:Dict       							=				pipe(
                  **UpperCAmelCase_  ,     return_dict=UpperCAmelCase_  ,     )[0]

              lowercase		:Union[str, Any]       							=				image[0, -3:, -3:, -1]
              lowercase		:str       							=				image_from_tuple[0, -3:, -3:, -1]

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

              lowercase		:Dict       							=				np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
              assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:int       							=				'cpu'

              lowercase		:int       							=				self.get_dummy_components()

              lowercase		:Tuple       							=				self.pipeline_class(**UpperCAmelCase_				)
              lowercase		:Dict       							=				pipe.to(UpperCAmelCase_				)

              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              lowercase		:Dict       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)
              lowercase		:Dict       							=				[
                  pipeline_inputs['image'],
                  pipeline_inputs['image'],
              ]

              lowercase		:int       							=				pipe(**UpperCAmelCase_				)
              lowercase		:Optional[int]       							=				output.images

              lowercase		:Tuple       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)
              lowercase		:List[str]       							=				[
                  tuple_pipeline_inputs['image'],
                  tuple_pipeline_inputs['image'],
              ]

              lowercase		:str       							=				pipe(
                  **UpperCAmelCase_  ,     return_dict=UpperCAmelCase_  ,     )[0]

              lowercase		:str       							=				image[0, -3:, -3:, -1]
              lowercase		:Optional[int]       							=				image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (2, 6_4, 6_4, 3)

              lowercase		:Any       							=				np.array(
                  [
                      0.99_97,
                      0.99_89,
                      0.00_08,
                      0.00_21,
                      0.99_60,
                      0.00_18,
                      0.00_14,
                      0.00_02,
                      0.99_33,
                  ]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
              assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				torch.device('''cpu'''				)
              class 					__magic_name__		:
                     __A			:      Optional[Any]        =    1







              lowercase		:List[Any]       							=				self.get_dummy_components()

              lowercase		:Dict       							=				self.pipeline_class(**UpperCAmelCase_				)
              lowercase		:str       							=				pipe.to(UpperCAmelCase_				)

              pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

              lowercase		:Dict       							=				torch.Generator(device=UpperCAmelCase_				).manual_seed(0				)
              lowercase		:Union[str, Any]       							=				pipe.decoder.dtype
              lowercase		:Optional[int]       							=				1

              lowercase		:Optional[Any]       							=				(
                  batch_size,
                  pipe.decoder.config.in_channels,
                  pipe.decoder.config.sample_size,
                  pipe.decoder.config.sample_size,
              )
              lowercase		:Optional[int]       							=				pipe.prepare_latents(
                  UpperCAmelCase_  ,     dtype=UpperCAmelCase_  ,     device=UpperCAmelCase_  ,     generator=UpperCAmelCase_  ,     latents=UpperCAmelCase_  ,     scheduler=DummyScheduler()				)

              lowercase		:Any       							=				(
                  batch_size,
                  pipe.super_res_first.config.in_channels // 2,
                  pipe.super_res_first.config.sample_size,
                  pipe.super_res_first.config.sample_size,
              )
              lowercase		:str       							=				pipe.prepare_latents(
                  UpperCAmelCase_  ,     dtype=UpperCAmelCase_  ,     device=UpperCAmelCase_  ,     generator=UpperCAmelCase_  ,     latents=UpperCAmelCase_  ,     scheduler=DummyScheduler()				)

              lowercase		:List[str]       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)

              lowercase		:Union[str, Any]       							=				pipe(
                  **UpperCAmelCase_  ,     decoder_latents=UpperCAmelCase_  ,     super_res_latents=UpperCAmelCase_				).images

              lowercase		:int       							=				self.get_dummy_inputs(UpperCAmelCase_  ,     pil_image=UpperCAmelCase_				)
              # Don't pass image, instead pass embedding
              lowercase		:Dict       							=				pipeline_inputs.pop('''image'''				)
              lowercase		:int       							=				pipe.image_encoder(UpperCAmelCase_				).image_embeds

              lowercase		:int       							=				pipe(
                  **UpperCAmelCase_  ,     decoder_latents=UpperCAmelCase_  ,     super_res_latents=UpperCAmelCase_  ,     image_embeddings=UpperCAmelCase_  ,     ).images

              # make sure passing text embeddings manually is identical
              assert np.abs(img_out_a - img_out_a				).max() < 1e-4






       @skip_mps
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				torch_device == 'cpu'

              # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
              lowercase		:Optional[Any]       							=				1e-2

              self._test_attention_slicing_forward_pass(
                  test_max_difference=UpperCAmelCase_  ,     expected_max_diff=UpperCAmelCase_				)






       @skip_mps
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				torch_device == 'cpu'
              lowercase		:Dict       							=				True
              lowercase		:Optional[int]       							=				[
                  'decoder_num_inference_steps',
                  'super_res_num_inference_steps',
              ]

              self._test_inference_batch_single_identical(
                  test_max_difference=UpperCAmelCase_  ,     relax_max_difference=UpperCAmelCase_  ,     additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,     )






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				[
                  'decoder_num_inference_steps',
                  'super_res_num_inference_steps',
              ]

              if torch_device == "mps":
                     # TODO: MPS errors with larger batch sizes
                     lowercase		:Tuple       							=				[2, 3]
                     self._test_inference_batch_consistent(
                         batch_sizes=UpperCAmelCase_  ,     additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,     )
              else:
                     self._test_inference_batch_consistent(
                         additional_params_copy_to_batched_inputs=UpperCAmelCase_				)






       @skip_mps
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              return super().test_save_load_local()







       @skip_mps
       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              return super().test_save_load_optional_components()







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






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






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







       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''				)
              lowercase		:Dict       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''				)

              lowercase		:Any       							=				UnCLIPImageVariationPipeline.from_pretrained(
                  '''kakaobrain/karlo-v1-alpha-image-variations'''  ,     torch_dtype=torch.floataa				)
              lowercase		:Dict       							=				pipeline.to(UpperCAmelCase_				)
              pipeline.set_progress_bar_config(disable=UpperCAmelCase_				)

              lowercase		:List[Any]       							=				torch.Generator(device='''cpu'''				).manual_seed(0				)
              lowercase		:Optional[int]       							=				pipeline(
                  UpperCAmelCase_  ,     generator=UpperCAmelCase_  ,     output_type='''np'''  ,     )

              lowercase		:List[Any]       							=				output.images[0]

              assert image.shape == (2_5_6, 2_5_6, 3)

              assert_mean_pixel_difference(UpperCAmelCase_  ,     UpperCAmelCase_  ,     1_5				)





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






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






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






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

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				make_linear_from_emb(model.model.shared)

       return model


if __name__ == "__main__":
 UpperCAmelCase      		=			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''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			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)

