





"""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   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       UnCLIPImageVariationPipeline
  SCREAMING_SNAKE_CASE__				:int						  =       IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
  SCREAMING_SNAKE_CASE__				:int						  =       IMAGE_VARIATION_BATCH_PARAMS

  SCREAMING_SNAKE_CASE__				:str						  =       [
      "generator",
      "return_dict",
      "decoder_num_inference_steps",
      "super_res_num_inference_steps",
  ]
  SCREAMING_SNAKE_CASE__				:Dict						  =       False

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					int:
        return 32

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Optional[Any]:
        return 32

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					Tuple:
        return self.time_input_dim

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Optional[Any]:
        return self.time_input_dim * 4

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Any:
        return 100

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Tuple:
        _UpperCamelCase   :						Union[str, Any]    =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
        return tokenizer

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        torch.manual_seed(0						)
        _UpperCamelCase   :						Optional[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=37       , layer_norm_eps=1e-0_5       , num_attention_heads=4       , num_hidden_layers=5       , pad_token_id=1       , vocab_size=1000       , )
        return CLIPTextModelWithProjection(__a						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					Tuple:
        torch.manual_seed(0						)
        _UpperCamelCase   :						List[Any]    =							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=32       , intermediate_size=37       , patch_size=1       , )
        return CLIPVisionModelWithProjection(__a						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					List[Any]:
        torch.manual_seed(0						)

        _UpperCamelCase   :						Any    =							{
            "clip_embeddings_dim": self.text_embedder_hidden_size,
            "time_embed_dim": self.time_embed_dim,
            "cross_attention_dim": self.cross_attention_dim,
        }

        _UpperCamelCase   :						List[Any]    =							UnCLIPTextProjModel(**__a						)
        return model

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        torch.manual_seed(0						)

        _UpperCamelCase   :						List[Any]    =							{
            "sample_size": 32,
            # 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",
        }

        _UpperCamelCase   :						Optional[Any]    =							UNetaDConditionModel(**__a						)
        return model

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        return {
            "sample_size": 64,
            "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  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					List[Any]:
        torch.manual_seed(0						)

        _UpperCamelCase   :						Tuple    =							UNetaDModel(**self.dummy_super_res_kwargs						)
        return model

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					Optional[Any]:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1						)

        _UpperCamelCase   :						str    =							UNetaDModel(**self.dummy_super_res_kwargs						)
        return model

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					str:
        _UpperCamelCase   :						Dict    =							self.dummy_decoder
        _UpperCamelCase   :						Tuple    =							self.dummy_text_proj
        _UpperCamelCase   :						str    =							self.dummy_text_encoder
        _UpperCamelCase   :						Tuple    =							self.dummy_tokenizer
        _UpperCamelCase   :						List[str]    =							self.dummy_super_res_first
        _UpperCamelCase   :						Any    =							self.dummy_super_res_last

        _UpperCamelCase   :						Optional[int]    =							UnCLIPScheduler(
            variance_type="learned_range"       , prediction_type="epsilon"       , num_train_timesteps=1000       , )

        _UpperCamelCase   :						int    =							UnCLIPScheduler(
            variance_type="fixed_small_log"       , prediction_type="epsilon"       , num_train_timesteps=1000       , )

        _UpperCamelCase   :						Optional[Any]    =							CLIPImageProcessor(crop_size=32       , size=32						)

        _UpperCamelCase   :						List[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  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]       , __a :							List[Any]       , __a :							Any=0       , __a :							Tuple=True						)		->					Optional[Any]:
        _UpperCamelCase   :						List[Any]    =							floats_tensor((1, 3, 32, 32)       , rng=random.Random(__a						)						).to(__a						)
        if str(__a						).startswith("mps"						):
              _UpperCamelCase   :						Tuple    =							torch.manual_seed(__a						)
        else:
              _UpperCamelCase   :						List[str]    =							torch.Generator(device=__a						).manual_seed(__a						)

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

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Optional[int]:
        _UpperCamelCase   :						str    =							"cpu"

        _UpperCamelCase   :						Optional[int]    =							self.get_dummy_components()

        _UpperCamelCase   :						Any    =							self.pipeline_class(**__a						)
        _UpperCamelCase   :						Optional[Any]    =							pipe.to(__a						)

        pipe.set_progress_bar_config(disable=__a						)

        _UpperCamelCase   :						Optional[Any]    =							self.get_dummy_inputs(__a       , pil_image=__a						)

        _UpperCamelCase   :						Union[str, Any]    =							pipe(**__a						)
        _UpperCamelCase   :						Dict    =							output.images

        _UpperCamelCase   :						List[Any]    =							self.get_dummy_inputs(__a       , pil_image=__a						)

        _UpperCamelCase   :						List[Any]    =							pipe(
            **__a       , return_dict=__a       , )[0]

        _UpperCamelCase   :						Tuple    =							image[0, -3:, -3:, -1]
        _UpperCamelCase   :						Optional[int]    =							image_from_tuple[0, -3:, -3:, -1]

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

        _UpperCamelCase   :						Any    =							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  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					List[str]:
        _UpperCamelCase   :						Dict    =							"cpu"

        _UpperCamelCase   :						Dict    =							self.get_dummy_components()

        _UpperCamelCase   :						Optional[int]    =							self.pipeline_class(**__a						)
        _UpperCamelCase   :						Optional[Any]    =							pipe.to(__a						)

        pipe.set_progress_bar_config(disable=__a						)

        _UpperCamelCase   :						List[str]    =							self.get_dummy_inputs(__a       , pil_image=__a						)

        _UpperCamelCase   :						Union[str, Any]    =							pipe(**__a						)
        _UpperCamelCase   :						List[Any]    =							output.images

        _UpperCamelCase   :						int    =							self.get_dummy_inputs(__a       , pil_image=__a						)

        _UpperCamelCase   :						Optional[Any]    =							pipe(
            **__a       , return_dict=__a       , )[0]

        _UpperCamelCase   :						List[Any]    =							image[0, -3:, -3:, -1]
        _UpperCamelCase   :						Any    =							image_from_tuple[0, -3:, -3:, -1]

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

        _UpperCamelCase   :						str    =							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  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					int:
        _UpperCamelCase   :						Union[str, Any]    =							"cpu"

        _UpperCamelCase   :						int    =							self.get_dummy_components()

        _UpperCamelCase   :						List[Any]    =							self.pipeline_class(**__a						)
        _UpperCamelCase   :						Optional[int]    =							pipe.to(__a						)

        pipe.set_progress_bar_config(disable=__a						)

        _UpperCamelCase   :						List[Any]    =							self.get_dummy_inputs(__a       , pil_image=__a						)
        _UpperCamelCase   :						Dict    =							[
            pipeline_inputs["image"],
            pipeline_inputs["image"],
        ]

        _UpperCamelCase   :						List[str]    =							pipe(**__a						)
        _UpperCamelCase   :						Optional[int]    =							output.images

        _UpperCamelCase   :						List[str]    =							self.get_dummy_inputs(__a       , pil_image=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							[
            tuple_pipeline_inputs["image"],
            tuple_pipeline_inputs["image"],
        ]

        _UpperCamelCase   :						List[Any]    =							pipe(
            **__a       , return_dict=__a       , )[0]

        _UpperCamelCase   :						Optional[Any]    =							image[0, -3:, -3:, -1]
        _UpperCamelCase   :						List[Any]    =							image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)

        _UpperCamelCase   :						int    =							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  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						List[Any]    =							torch.device("cpu"						)



        class   __SCREAMING_SNAKE_CASE     :







          '''simple docstring'''


          SCREAMING_SNAKE_CASE__				:str						  =       1





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

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

        pipe.set_progress_bar_config(disable=__a						)

        _UpperCamelCase   :						List[Any]    =							torch.Generator(device=__a						).manual_seed(0						)
        _UpperCamelCase   :						List[Any]    =							pipe.decoder.dtype
        _UpperCamelCase   :						Optional[int]    =							1

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

        _UpperCamelCase   :						Union[str, 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,
        )
        _UpperCamelCase   :						Tuple    =							pipe.prepare_latents(
            __a       , dtype=__a       , device=__a       , generator=__a       , latents=__a       , scheduler=DummyScheduler()						)

        _UpperCamelCase   :						Tuple    =							self.get_dummy_inputs(__a       , pil_image=__a						)

        _UpperCamelCase   :						Optional[Any]    =							pipe(
            **__a       , decoder_latents=__a       , super_res_latents=__a						).images

        _UpperCamelCase   :						Optional[int]    =							self.get_dummy_inputs(__a       , pil_image=__a						)
        # Don't pass image, instead pass embedding
        _UpperCamelCase   :						Optional[Any]    =							pipeline_inputs.pop("image"						)
        _UpperCamelCase   :						Dict    =							pipe.image_encoder(__a						).image_embeds

        _UpperCamelCase   :						Optional[int]    =							pipe(
            **__a       , decoder_latents=__a       , super_res_latents=__a       , image_embeddings=__a       , ).images

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

  @skip_mps
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch_device == "cpu"

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        _UpperCamelCase   :						List[str]    =							1e-2

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

  @skip_mps
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					List[Any]:
        _UpperCamelCase   :						Optional[int]    =							torch_device == "cpu"
        _UpperCamelCase   :						List[Any]    =							True
        _UpperCamelCase   :						Dict    =							[
            "decoder_num_inference_steps",
            "super_res_num_inference_steps",
        ]

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

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Dict:
        _UpperCamelCase   :						List[str]    =							[
            "decoder_num_inference_steps",
            "super_res_num_inference_steps",
        ]

        if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              _UpperCamelCase   :						Union[str, Any]    =							[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__a       , additional_params_copy_to_batched_inputs=__a       , )
        else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__a						)

  @skip_mps
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					int:
        return super().test_dict_tuple_outputs_equivalent()

  @skip_mps
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Optional[Any]:
        return super().test_save_load_local()


  @skip_mps
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					int:
        return super().test_save_load_optional_components()





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







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Optional[int]:
        _UpperCamelCase   :						List[Any]    =							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"						)
        _UpperCamelCase   :						Any    =							load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"						)

        _UpperCamelCase   :						Any    =							UnCLIPImageVariationPipeline.from_pretrained(
            "kakaobrain/karlo-v1-alpha-image-variations"       , torch_dtype=torch.floataa						)
        _UpperCamelCase   :						List[str]    =							pipeline.to(__a						)
        pipeline.set_progress_bar_config(disable=__a						)

        _UpperCamelCase   :						Tuple    =							torch.Generator(device="cpu"						).manual_seed(0						)
        _UpperCamelCase   :						Optional[Any]    =							pipeline(
            __a       , generator=__a       , output_type="np"       , )

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

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(__a       , __a       , 15						)







"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "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        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= 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__						= parser.parse_args()
  lowerCamelCase__						= 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)
