import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_:					Tuple     =					logging.get_logger(__name__)

lowerCAmelCase_:					Dict     =					{
    """post_extract_proj""": """feature_projection.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.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase_:					Union[str, Any]     =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     for attribute in key.split("."      ):
          lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      )

     if weight_type is not None:
          lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      ).shape
     else:
          lowercase__				 =							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":
          lowercase__				 =							value
     elif weight_type == "weight_g":
          lowercase__				 =							value
     elif weight_type == "weight_v":
          lowercase__				 =							value
     elif weight_type == "bias":
          lowercase__				 =							value
     else:
          lowercase__				 =							value

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







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							[]
     lowercase__				 =							fairseq_model.state_dict()

     lowercase__				 =							hf_model.feature_extractor
     lowercase__				 =							hf_model.adapter

     for name, value in fairseq_dict.items():
          lowercase__				 =							False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				hf_model.config.feat_extract_norm == "group"    ,				)
               lowercase__				 =							True
          elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]      ):
               load_adapter(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase      )
               lowercase__				 =							True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         lowercase__				 =							True
                         if "*" in mapped_key:
                              lowercase__				 =							name.split(_lowerCamelCase      )[0].split("."      )[-2]
                              lowercase__				 =							mapped_key.replace("*"    ,				_lowerCamelCase      )
                         if "weight_g" in name:
                              lowercase__				 =							"weight_g"
                         elif "weight_v" in name:
                              lowercase__				 =							"weight_v"
                         elif "bias" in name:
                              lowercase__				 =							"bias"
                         elif "weight" in name:
                              lowercase__				 =							"weight"
                         else:
                              lowercase__				 =							None
                         set_recursively(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase      )
                    continue
          if not is_used:
               unused_weights.append(_lowerCamelCase      )

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







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							full_name.split("conv_layers."      )[-1]
     lowercase__				 =							name.split("."      )
     lowercase__				 =							int(items[0]      )
     lowercase__				 =							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.'''
               )
               lowercase__				 =							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.'''
               )
               lowercase__				 =							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."
               )
               lowercase__				 =							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.'''
               )
               lowercase__				 =							value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
     else:
          unused_weights.append(_lowerCamelCase      )







def 					__a   (		A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							full_name.split("adaptor."      )[-1]
     lowercase__				 =							name.split("."      )

     if items[1].isdigit():
          lowercase__				 =							int(items[1]      )
     else:
          lowercase__				 =							None

     if "adaptor" not in full_name:
          if "proj_ln" in full_name:
               # has to be layer norm
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.bias.data.shape
                    ), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                    lowercase__				 =							value
                    logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.weight.data.shape
                    ), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                    lowercase__				 =							value
          else:
               # has to be projection layer
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj.bias.data.shape
                    ), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                    lowercase__				 =							value
                    logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''      )
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj.weight.data.shape
                    ), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                    lowercase__				 =							value
                    logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''      )
     elif isinstance(_lowerCamelCase    ,				_lowerCamelCase      ):
          if "bias" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.bias.data.shape
               ), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
               lowercase__				 =							value
               logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
          elif "weight" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.weight.data.shape
               ), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
               lowercase__				 =							value
               logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
     else:
          unused_weights.append(_lowerCamelCase      )







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__ ,						lowercase__				 =							emb.weight.shape
     lowercase__				 =							nn.Linear(_lowerCamelCase    ,				_lowerCamelCase    ,				bias=_lowerCamelCase      )
     lowercase__				 =							emb.weight.data
     return lin_layer







@torch.no_grad()
def 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				):






     '''simple docstring'''



     lowercase__				 =							WavaVecaConfig.from_pretrained(
         _lowerCamelCase    ,				add_adapter=_lowerCamelCase    ,				adapter_stride=_lowerCamelCase    ,				adapter_kernel_size=_lowerCamelCase    ,				use_auth_token=_lowerCamelCase    ,				output_hidden_size=_lowerCamelCase    ,				)
     lowercase__				 =							MBartConfig.from_pretrained(_lowerCamelCase      )

     # load model
     lowercase__ ,						lowercase__ ,						lowercase__				 =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]    ,				arg_overrides={
             "config_yaml": config_yaml_path,
             "data": "/".join(dict_path.split("/"      )[:-1]      ),
             "w2v_path": checkpoint_path,
             "load_pretrained_decoder_from": None,
         }    ,				)
     lowercase__				 =							model[0].eval()

     # load feature extractor
     lowercase__				 =							WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase    ,				use_auth_token=_lowerCamelCase      )

     # set weights for wav2vec2 encoder
     lowercase__				 =							WavaVecaModel(_lowerCamelCase      )

     recursively_load_weights_wavaveca(model.encoder    ,				_lowerCamelCase      )

     # load decoder weights
     lowercase__				 =							MBartForCausalLM(_lowerCamelCase      )
     lowercase__ ,						lowercase__				 =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,				strict=_lowerCamelCase      )
     logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''      )
     logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''      )

     lowercase__				 =							SpeechEncoderDecoderModel(encoder=_lowerCamelCase    ,				decoder=_lowerCamelCase      )
     lowercase__				 =							False

     lowercase__				 =							MBartaaTokenizer(_lowerCamelCase      )
     tokenizer.save_pretrained(_lowerCamelCase      )

     lowercase__				 =							hf_wavavec.config.to_dict()
     lowercase__				 =							tokenizer.pad_token_id
     lowercase__				 =							tokenizer.bos_token_id
     lowercase__				 =							tokenizer.eos_token_id
     lowercase__				 =							"mbart50"
     lowercase__				 =							"wav2vec2"

     lowercase__				 =							tokenizer.eos_token_id
     lowercase__				 =							25_00_04
     lowercase__				 =							tokenizer.eos_token_id

     lowercase__				 =							SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase      )

     hf_wavavec.save_pretrained(_lowerCamelCase      )
     feature_extractor.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
 lowerCAmelCase_:					Optional[int]     =					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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
 parser.add_argument(
     "--encoder_config_path",
     default="facebook/wav2vec2-xls-r-1b",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/mbart-large-50-one-to-many-mmt",
     type=str,
     help="Path to hf decoder checkpoint config",
 )
 parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
 parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
 parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
 parser.add_argument("--encoder_output_dim", default=1_0_2_4, type=int, help="encoder output dim")
 parser.add_argument("--start_token_id", default=2_5_0_0_0_4, type=int, help="`decoder_start_token_id` of model config")

 lowerCAmelCase_:					Optional[Any]     =					parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )





"""simple docstring"""


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							hf_hub_download(
            repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							VideoClassificationPipeline(model=_UpperCAmelCase,				image_processor=_UpperCAmelCase,				top_k=2  )
        lowercase__				 =							[
            example_video_filepath,
            "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
        ]
        return video_classifier, examples


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

             self.assertEqual(
                 _UpperCAmelCase,				[
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                 ],				)


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




