






'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin






@skip_mps
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   IFPipeline
  lowercase  	=   TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
  lowercase  	=   TEXT_TO_IMAGE_BATCH_PARAMS
  lowercase  	=   PipelineTesterMixin.required_optional_params - {"""latents"""}

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return self._get_dummy_components()

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0   )						->   Union[str, Any]:



        """simple docstring"""






        if str(_SCREAMING_SNAKE_CASE   ).startswith("""mps"""   ):
              UpperCamelCase       						=      torch.manual_seed(_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      torch.Generator(device=_SCREAMING_SNAKE_CASE   ).manual_seed(_SCREAMING_SNAKE_CASE   )

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

        return inputs

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        self._test_save_load_optional_components()

  @unittest.skipIf(torch_device != """cuda"""    ,	reason="""float16 requires CUDA"""   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        super().test_save_load_floataa(expected_max_diff=1e-1   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self._test_save_load_local()

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2    ,	)


  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()    ,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,	)
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""    ,	variant="""fp16"""    ,	torch_dtype=torch.floataa   )

        UpperCamelCase       						=      IFSuperResolutionPipeline.from_pretrained(
            """DeepFloyd/IF-II-L-v1.0"""    ,	variant="""fp16"""    ,	torch_dtype=torch.floataa    ,	text_encoder=_SCREAMING_SNAKE_CASE    ,	tokenizer=_SCREAMING_SNAKE_CASE   )

        # pre compute text embeddings and remove T5 to save memory

        pipe_a.text_encoder.to("""cuda"""   )

        UpperCamelCase						,UpperCamelCase       						=      pipe_a.encode_prompt("""anime turtle"""    ,	device="""cuda"""   )

        del pipe_a.tokenizer
        del pipe_a.text_encoder
        gc.collect()

        UpperCamelCase       						=      None
        UpperCamelCase       						=      None

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # img2img

        UpperCamelCase       						=      IFImgaImgPipeline(**pipe_a.components   )
        UpperCamelCase       						=      IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if_imgaimg(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # inpainting

        UpperCamelCase       						=      IFInpaintingPipeline(**pipe_a.components   )
        UpperCamelCase       						=      IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if_inpainting(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        _start_torch_memory_measurement()

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )
        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	generator=_SCREAMING_SNAKE_CASE    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 13 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # pipeline 2

        _start_torch_memory_measurement()

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )

        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        _start_torch_memory_measurement()

        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )

        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	generator=_SCREAMING_SNAKE_CASE    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 10 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # pipeline 2

        _start_torch_memory_measurement()

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )

        UpperCamelCase       						=      floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	original_image=_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        _start_torch_memory_measurement()

        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(1   )   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )
        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	mask_image=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	generator=_SCREAMING_SNAKE_CASE    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 10 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # pipeline 2

        _start_torch_memory_measurement()

        UpperCamelCase       						=      torch.Generator(device="""cpu"""   ).manual_seed(0   )

        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(0   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(1   )   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      pipe_a(
            prompt_embeds=_SCREAMING_SNAKE_CASE    ,	negative_prompt_embeds=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	mask_image=_SCREAMING_SNAKE_CASE    ,	original_image=_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=2    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

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

        UpperCamelCase       						=      torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""   )
        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Tuple:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='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.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
