


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     for attribute in key.split('.' ):
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase )

     if weight_type is not None:
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase ).shape
     else:
          __UpperCamelCase =			hf_pointer.shape

     assert hf_shape == value.shape, (
         f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
         f''' {value.shape} for {full_name}'''
     )

     if weight_type == "weight":
          __UpperCamelCase =			value
     elif weight_type == "weight_g":
          __UpperCamelCase =			value
     elif weight_type == "weight_v":
          __UpperCamelCase =			value
     elif weight_type == "bias":
          __UpperCamelCase =			value
     else:
          __UpperCamelCase =			value

     logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			fairseq_model.state_dict()

     __UpperCamelCase =			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __UpperCamelCase =			False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  hf_model.config.feat_extract_norm == 'group'					,  )
               __UpperCamelCase =			True
          else:
               for key, mapped_key in MAPPING.items():
                    __UpperCamelCase =			'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                    if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
                         __UpperCamelCase =			True
                         if "*" in mapped_key:
                              __UpperCamelCase =			name.split(_lowercase )[0].split('.' )[-2]
                              __UpperCamelCase =			mapped_key.replace('*'					,  _lowercase )
                         if "weight_g" in name:
                              __UpperCamelCase =			'weight_g'
                         elif "weight_v" in name:
                              __UpperCamelCase =			'weight_v'
                         elif "weight" in name:
                              __UpperCamelCase =			'weight'
                         elif "bias" in name:
                              __UpperCamelCase =			'bias'
                         else:
                              __UpperCamelCase =			None
                         set_recursively(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
                    continue
          if not is_used:
               unused_weights.append(_lowercase )

     logger.warning(f'''Unused weights: {unused_weights}''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			full_name.split('conv_layers.' )[-1]
     __UpperCamelCase =			name.split('.' )
     __UpperCamelCase =			int(items[0] )
     __UpperCamelCase =			int(items[1] )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
     else:
          unused_weights.append(_lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			SEWConfig()
     if is_finetuned:
          __UpperCamelCase =			model.wav_encoder.wav_model.cfg
     else:
          __UpperCamelCase =			model.cfg

     __UpperCamelCase =			fs_config.conv_bias
     __UpperCamelCase =			eval(fs_config.conv_feature_layers )
     __UpperCamelCase =			[x[0] for x in conv_layers]
     __UpperCamelCase =			[x[1] for x in conv_layers]
     __UpperCamelCase =			[x[2] for x in conv_layers]
     __UpperCamelCase =			'gelu'
     __UpperCamelCase =			'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     __UpperCamelCase =			0.0
     __UpperCamelCase =			fs_config.activation_fn.name
     __UpperCamelCase =			fs_config.encoder_embed_dim
     __UpperCamelCase =			0.02
     __UpperCamelCase =			fs_config.encoder_ffn_embed_dim
     __UpperCamelCase =			1e-5
     __UpperCamelCase =			fs_config.encoder_layerdrop
     __UpperCamelCase =			fs_config.encoder_attention_heads
     __UpperCamelCase =			fs_config.conv_pos_groups
     __UpperCamelCase =			fs_config.conv_pos
     __UpperCamelCase =			len(_lowercase )
     __UpperCamelCase =			fs_config.encoder_layers
     __UpperCamelCase =			fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          __UpperCamelCase =			model.cfg
          __UpperCamelCase =			fs_config.final_dropout
          __UpperCamelCase =			fs_config.layerdrop
     __UpperCamelCase =			fs_config.activation_dropout
     __UpperCamelCase =			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     __UpperCamelCase =			fs_config.attention_dropout
     __UpperCamelCase =			fs_config.dropout_input
     __UpperCamelCase =			fs_config.dropout
     __UpperCamelCase =			fs_config.mask_channel_length
     __UpperCamelCase =			fs_config.mask_channel_prob
     __UpperCamelCase =			fs_config.mask_length
     __UpperCamelCase =			fs_config.mask_prob

     __UpperCamelCase =			'Wav2Vec2FeatureExtractor'
     __UpperCamelCase =			'Wav2Vec2CTCTokenizer'

     return config






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None					,  _lowercase=True )					-> Optional[Any]:


     """simple docstring"""


     if is_finetuned:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]					,  arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
     else:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

     if config_path is not None:
          __UpperCamelCase =			SEWConfig.from_pretrained(_lowercase )
     else:
          __UpperCamelCase =			convert_config(model[0]					,  _lowercase )
     __UpperCamelCase =			model[0].eval()

     __UpperCamelCase =			True if config.feat_extract_norm == 'layer' else False
     __UpperCamelCase =			WavaVecaFeatureExtractor(
         feature_size=1					,  sampling_rate=1_60_00					,  padding_value=0					,  do_normalize=_lowercase					,  return_attention_mask=_lowercase					,  )

     if is_finetuned:
          if dict_path:
               __UpperCamelCase =			Dictionary.load(_lowercase )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __UpperCamelCase =			target_dict.pad_index
               __UpperCamelCase =			target_dict.bos_index
               __UpperCamelCase =			target_dict.pad_index
               __UpperCamelCase =			target_dict.bos_index
               __UpperCamelCase =			target_dict.eos_index
               __UpperCamelCase =			len(target_dict.symbols )
               __UpperCamelCase =			os.path.join(_lowercase					,  'vocab.json' )
               if not os.path.isdir(_lowercase ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowercase ) )
                    return
               os.makedirs(_lowercase					,  exist_ok=_lowercase )
               with open(_lowercase					,  'w'					,  encoding='utf-8' ) as vocab_handle:
                    json.dump(target_dict.indices					,  _lowercase )
               __UpperCamelCase =			WavaVecaCTCTokenizer(
                   _lowercase					,  unk_token=target_dict.unk_word					,  pad_token=target_dict.pad_word					,  bos_token=target_dict.bos_word					,  eos_token=target_dict.eos_word					,  word_delimiter_token='|'					,  do_lower_case=_lowercase					,  )
               __UpperCamelCase =			WavaVecaProcessor(feature_extractor=_lowercase					,  tokenizer=_lowercase )
               processor.save_pretrained(_lowercase )

          __UpperCamelCase =			SEWForCTC(_lowercase )
     else:
          __UpperCamelCase =			SEWModel(_lowercase )
          feature_extractor.save_pretrained(_lowercase )

     recursively_load_weights(_lowercase					,  _lowercase					,  _lowercase )

     hf_model.save_pretrained(_lowercase )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    __snake_case   							=						parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )






import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

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


enable_full_determinism()



@skip_mps
class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     VideoToVideoSDPipeline
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}						) - {"""image""", """width""", """height"""}
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}						) - {"""image"""}
       _lowercase											=     PipelineTesterMixin.required_optional_params - {"""latents"""}
       _lowercase											=     False

       # No `output_type`.
       _lowercase											=     frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]						)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			UNetaDConditionModel(
                block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D'),up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D'),cross_attention_dim=32,attention_head_dim=4,)
            __UpperCamelCase =			DDIMScheduler(
                beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,beta_schedule='scaled_linear',clip_sample=A_,set_alpha_to_one=A_,)
            torch.manual_seed(0					)
            __UpperCamelCase =			AutoencoderKL(
                block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=128,)
            torch.manual_seed(0					)
            __UpperCamelCase =			CLIPTextConfig(
                bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,hidden_act='gelu',projection_dim=512,)
            __UpperCamelCase =			CLIPTextModel(A_					)
            __UpperCamelCase =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

            __UpperCamelCase =			{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
            }
            return components







       def 		snake_case_	(			self: Union[str, Any],A_: Any,A_: Any=0					):






            '''simple docstring'''




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

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'A painting of a squirrel eating a burger',
                'video': video,
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'pt',
            }
            return inputs







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase =			self.get_dummy_components()
            __UpperCamelCase =			VideoToVideoSDPipeline(**A_					)
            __UpperCamelCase =			sd_pipe.to(A_					)
            sd_pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			self.get_dummy_inputs(A_					)
            __UpperCamelCase =			'np'
            __UpperCamelCase =			sd_pipe(**A_					).frames
            __UpperCamelCase =			frames[0][-3:, -3:, -1]

            assert frames[0].shape == (32, 32, 3)
            __UpperCamelCase =			np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

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







       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available(),reason='XFormers attention is only available with CUDA and `xformers` installed',)
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_,expected_max_diff=5E-3					)







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'					)
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return super().test_progress_bar()



@slow
@skip_mps
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL',torch_dtype=torch.floataa					)
            pipe.enable_model_cpu_offload()

            # 10 frames
            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase =			torch.randn((1, 10, 3, 1024, 576),generator=A_					)
            __UpperCamelCase =			video.to('cuda'					)

            __UpperCamelCase =			'Spiderman is surfing'

            __UpperCamelCase =			pipe(A_,video=A_,generator=A_,num_inference_steps=3,output_type='pt'					).frames

            __UpperCamelCase =			np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]					)
            assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1E-2


