





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_UpperCAmelCase				:       Optional[Any]	   =				False

class    lowerCAmelCase							(					unittest.TestCase			):
 pass


@nightly
@require_torch_gpu
class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      str	)			->	Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'			,     torch_dtype=torch.floataa	)
     pipe.to(UpperCAmelCase	)
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

     lowerCamelCase__    :      int					 =		torch.manual_seed(0	)
     lowerCamelCase__    :      List[Any]					 =		pipe.dual_guided(
         prompt='first prompt'			,     image=UpperCAmelCase			,     text_to_image_strength=0.7_5			,     generator=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=2			,     output_type='numpy'			,     ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(UpperCAmelCase	)
         lowerCamelCase__    :      Union[str, Any]					 =		VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase			,     torch_dtype=torch.floataa	)
     pipe.to(UpperCAmelCase	)
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      Any					 =		generator.manual_seed(0	)
     lowerCamelCase__    :      int					 =		pipe.dual_guided(
         prompt='first prompt'			,     image=UpperCAmelCase			,     text_to_image_strength=0.7_5			,     generator=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=2			,     output_type='numpy'			,     ).images

     assert np.abs(image - new_image	).sum() < 1e-5, "Models don't have the same forward pass"







 def      A_	(      self  :      str	)			->	int:
     lowerCamelCase__    :      str					 =		VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'			,     torch_dtype=torch.floataa	)
     pipe.to(UpperCAmelCase	)
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		'cyberpunk 2077'
     lowerCamelCase__    :      Any					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
     lowerCamelCase__    :      List[str]					 =		torch.manual_seed(0	)
     lowerCamelCase__    :      List[str]					 =		pipe.dual_guided(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     text_to_image_strength=0.7_5			,     generator=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=50			,     output_type='numpy'			,     ).images

     lowerCamelCase__    :      Optional[int]					 =		image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Tuple					 =		np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]	)

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

     lowerCamelCase__    :      Union[str, Any]					 =		'A painting of a squirrel eating a burger '
     lowerCamelCase__    :      Dict					 =		torch.manual_seed(0	)
     lowerCamelCase__    :      int					 =		pipe.text_to_image(
         prompt=UpperCAmelCase			,     generator=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=50			,     output_type='numpy'	).images

     lowerCamelCase__    :      Union[str, Any]					 =		image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Optional[Any]					 =		np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]	)

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

     lowerCamelCase__    :      Optional[Any]					 =		pipe.image_variation(UpperCAmelCase			,     generator=UpperCAmelCase			,     output_type='numpy'	).images

     lowerCamelCase__    :      Dict					 =		image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Optional[int]					 =		np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]	)

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












import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        '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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		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'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				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.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





