
import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							lowerCamelCase       (		__snake_case			,  __snake_case			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		IFImgaImgPipeline
  lowerCAmelCase_								=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_								=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_								=		PipelineTesterMixin.required_optional_params - {"""latents"""}

  def 					lowercase_						(					self					):
      return self._get_dummy_components()

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=0					):
      if str(__UpperCamelCase					).startswith("mps"					):
          A_								=					torch.manual_seed(__UpperCamelCase					)
      else:
          A_								=					torch.Generator(device=__UpperCamelCase					).manual_seed(__UpperCamelCase					)

      A_								=					floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__UpperCamelCase					)					).to(__UpperCamelCase					)

      A_								=					{
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs

  def 					lowercase_						(					self					):
      self._test_save_load_optional_components()

  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()						,    reason="XFormers attention is only available with CUDA and `xformers` installed"						,    )
  def 					lowercase_						(					self					):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)

  @unittest.skipIf(torch_device != "cuda"						,    reason="float16 requires CUDA"					)
  def 					lowercase_						(					self					):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1					)

  @unittest.skipIf(torch_device != "cuda"						,    reason="float16 requires CUDA"					)
  def 					lowercase_						(					self					):
      super().test_floataa_inference(expected_max_diff=1E-1					)

  def 					lowercase_						(					self					):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)

  def 					lowercase_						(					self					):
      self._test_save_load_local()

  def 					lowercase_						(					self					):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2						,    )








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='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='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






