


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_snake_case	:						Tuple         =							False

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							pass

@nightly
@require_torch_gpu
class 							a						(unittest.TestCase ):




							"""simple docstring"""


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

							def        __snake_case   (				self			:    Any							)      ->       Dict:
														__snake_case						:     Optional[Any]									=  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"							)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  "A painting of a squirrel eating a burger "
														__snake_case						:     str									=  torch.manual_seed(0							)
														__snake_case						:     str									=  pipe(
														    prompt=lowerCamelCase       ,      generator=lowerCamelCase       ,      guidance_scale=7.5       ,      num_inference_steps=2       ,      output_type="numpy"							).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase							)
																					__snake_case						:     str									=  VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase							)
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     Tuple									=  generator.manual_seed(0							)
														__snake_case						:     int									=  pipe(
														    prompt=lowerCamelCase       ,      generator=lowerCamelCase       ,      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        __snake_case   (				self			:    Dict							)      ->       Dict:
														__snake_case						:     Any									=  VersatileDiffusionTextToImagePipeline.from_pretrained(
														    "shi-labs/versatile-diffusion"       ,      torch_dtype=torch.floataa							)
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  "A painting of a squirrel eating a burger "
														__snake_case						:     Tuple									=  torch.manual_seed(0							)
														__snake_case						:     int									=  pipe(
														    prompt=lowerCamelCase       ,      generator=lowerCamelCase       ,      guidance_scale=7.5       ,      num_inference_steps=50       ,      output_type="numpy"							).images

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

														assert image.shape == (1, 512, 512, 3)
														__snake_case						:     List[Any]									=  np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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



"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      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(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,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__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					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='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__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

