



"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    [
        '''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  lowerCamelCase_						(UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Optional[int]											=    list(s_dict.keys()							)
    for key in keys:
        if "transformer_layers" in key:
            _UpperCAmelCase							:  Any											=    s_dict.pop(UpperCamelCase__							)
        elif "subsample" in key:
            _UpperCAmelCase							:  str											=    s_dict.pop(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    emb.weight.shape
    _UpperCAmelCase							:  Optional[int]											=    nn.Linear(UpperCamelCase__     ,       UpperCamelCase__     ,       bias=UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    emb.weight.data
    return lin_layer





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  Dict											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)
    _UpperCAmelCase							:  Optional[int]											=    mam_aaa['''args''']
    _UpperCAmelCase							:  Dict											=    mam_aaa['''model''']
    _UpperCAmelCase							:  Any											=    state_dict['''decoder.output_projection.weight''']

    remove_ignore_keys_(UpperCamelCase__							)
    rename_keys(UpperCamelCase__							)

    _UpperCAmelCase							:  str											=    state_dict['''decoder.embed_tokens.weight'''].shape[0]

    _UpperCAmelCase							:  Optional[int]											=    args.share_decoder_input_output_embed

    _UpperCAmelCase							:  Any											=    [int(UpperCamelCase__							) for i in args.conv_kernel_sizes.split(''','''							)]
    _UpperCAmelCase							:  Dict											=    SpeechaTextConfig(
        vocab_size=UpperCamelCase__     ,       max_source_positions=args.max_source_positions     ,       max_target_positions=args.max_target_positions     ,       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     ,       dropout=args.dropout     ,       attention_dropout=args.attention_dropout     ,       activation_dropout=args.activation_dropout     ,       activation_function='''relu'''     ,       num_conv_layers=len(UpperCamelCase__							)     ,       conv_channels=args.conv_channels     ,       conv_kernel_sizes=UpperCamelCase__     ,       input_feat_per_channel=args.input_feat_per_channel     ,       input_channels=args.input_channels     ,       tie_word_embeddings=UpperCamelCase__     ,       num_beams=5     ,       max_length=200     ,       use_cache=UpperCamelCase__     ,       decoder_start_token_id=2     ,       early_stopping=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Tuple											=    SpeechaTextForConditionalGeneration(UpperCamelCase__							)
    _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    model.model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if len(UpperCamelCase__							) > 0 and not set(UpperCamelCase__							) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
            F' but all the following weights are missing {missing}'							)

    if tie_embeds:
        _UpperCAmelCase							:  List[Any]											=    make_linear_from_emb(model.model.decoder.embed_tokens							)
    else:
        _UpperCAmelCase							:  Any											=    lm_head_weights

    model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  _lowerCAmelCase    :Optional[Any]     =					parser.parse_args()
  convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







"""simple docstring"""
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


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



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





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

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

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	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  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


