



'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 99_8001     ):
		SCREAMING_SNAKE_CASE_						:Any										=      0
		for i in range(999 ,		99 ,		-1     ):  # 3 digit numbers range from 999 down to 100
				for j in range(999 ,		99 ,		-1     ):
						SCREAMING_SNAKE_CASE_						:Tuple										=      str(i * j     )
						if product_string == product_string[::-1] and i * j < n:
								SCREAMING_SNAKE_CASE_						:List[Any]										=      max(SCREAMING_SNAKE_CASE ,		i * j     )
		return answer


if __name__ == "__main__":
							print(f"""{solution() = }""")








import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

lowerCamelCase					    =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase					    =	[
    '''lm_head''',
]
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



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

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

    if hf_shape != value.shape:
        raise ValueError(
            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 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						[]
    _lowercase     			=						fairseq_model.state_dict()

    if not is_headless:
        _lowercase     			=						hf_model.dataavec_audio.feature_extractor
        _lowercase     			=						hf_model.dataavec_audio.encoder.pos_conv_embed

    else:
        _lowercase     			=						hf_model.feature_extractor
        _lowercase     			=						hf_model.encoder.pos_conv_embed

    for name, value in fairseq_dict.items():
        _lowercase     			=						False
        if "conv_layers" in name:
            load_conv_layer(
                _lowercase,   _lowercase,   _lowercase,   _lowercase,   )
            _lowercase     			=						True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                _lowercase,   _lowercase,   _lowercase,   _lowercase,   )
            _lowercase     			=						True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    _lowercase     			=						'data2vec_audio.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
                    _lowercase     			=						True
                    if "*" in mapped_key:
                        _lowercase     			=						name.split(_lowercase   )[0].split('.'   )[-2]
                        _lowercase     			=						mapped_key.replace('*',   _lowercase   )
                    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:
                        # TODO: don't match quantizer.weight_proj
                        _lowercase     			=						'weight'
                    else:
                        _lowercase     			=						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 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						path.split('.'   )
    return reduce(_lowercase,   _lowercase,   _lowercase   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						access_by_string(_lowercase,   _lowercase   )
    _lowercase     			=						hf_weight.data

    if fsq_value.shape != hf_value.shape:
        raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''   )
    _lowercase     			=						fsq_value
    logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



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

    _lowercase     			=						name.split('.'   )[-1]
    if type_id == 0:
        _lowercase     			=						'conv'
    elif type_id == 2:
        _lowercase     			=						'layer_norm'
    else:
        unused_weights.append(_lowercase   )
        return

    set_weights(_lowercase,   _lowercase,   _lowercase,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						full_name.split('pos_conv.'   )[-1]
    _lowercase     			=						name.split('.'   )
    _lowercase     			=						int(items[0]   )
    _lowercase     			=						int(items[1]   )

    _lowercase     			=						name.split('.'   )[-1]
    if type_id != 0:
        unused_weights.append(_lowercase   )
        return
    else:
        _lowercase     			=						'conv'

    set_weights(_lowercase,   _lowercase,   _lowercase,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''   )
@torch.no_grad()
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase=None,   _lowercase=None,   _lowercase=True   ):


    '''simple docstring'''



    if config_path is not None:
        _lowercase     			=						DataaVecAudioConfig.from_pretrained(_lowercase   )
    else:
        _lowercase     			=						DataaVecAudioConfig()

    if not is_finetuned:
        # Modify final_proj layer name
        _lowercase     			=						DataaVecAudioModel(_lowercase   )
        _lowercase     			=						os.path.dirname(_lowercase   )

        _lowercase     			=						torch.load(_lowercase   )
        _lowercase     			=						state_dict['model'].pop('final_proj.0.weight'   )
        _lowercase     			=						state_dict['model'].pop('final_proj.0.bias'   )
        _lowercase     			=						os.path.join(_lowercase,   'converted.pt'   )
        torch.save(_lowercase,   _lowercase   )
    else:
        _lowercase     			=						DataaVecAudioForCTC(_lowercase   )
        _lowercase     			=						checkpoint_path

    def load_dataavec(_lowercase   ):
        _lowercase			,     _lowercase			,     _lowercase     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([path]   )
        return model[0].eval()

    _lowercase     			=						load_dataavec(_lowercase   )

    recursively_load_weights(_lowercase,   _lowercase,   not is_finetuned   )

    _lowercase     			=						WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'   )

    _lowercase     			=						load_dataset('patrickvonplaten/librispeech_asr_dummy',   'clean',   split='validation'   )
    _lowercase     			=						[x['array'] for x in ds[:4]['audio']]

    _lowercase     			=						processor(_lowercase,   return_tensors='pt',   padding=_lowercase   )

    _lowercase     			=						inputs.input_values
    _lowercase     			=						inputs.attention_mask
    #    input_values = inputs.input_values[:, :-1]
    #    attention_mask = inputs.attention_mask[:, :-1]

    hf_wavavec.eval()
    model.eval()
    if is_finetuned:
        _lowercase     			=						model(source=_lowercase,   padding_mask=(1 - attention_mask),   mask=_lowercase,   features_only=_lowercase   )[
            'encoder_out'
        ].transpose(0,   1   )
        _lowercase     			=						hf_wavavec(_lowercase,   attention_mask=_lowercase   )['logits']

        _lowercase     			=						torch.argmax(_lowercase,   dim=-1   )
        _lowercase     			=						processor.batch_decode(_lowercase   )

        print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''   )
    else:
        _lowercase     			=						model(source=_lowercase,   padding_mask=(1 - attention_mask),   mask=_lowercase,   features_only=_lowercase   )[
            'layer_results'
        ][-1][0].transpose(0,   1   )
        _lowercase     			=						hf_wavavec(_lowercase,   attention_mask=_lowercase   )['last_hidden_state']

    print(our_output.shape,   their_output.shape   )
    _lowercase     			=						torch.max(torch.abs(our_output - their_output   )   ).item()
    print(f'''max_absolute_diff = {max_absolute_diff}'''   )  # ~ 1e-7
    _lowercase     			=						torch.allclose(_lowercase,   _lowercase,   atol=1e-3   )
    print('Do both models output the same tensors?',   '🔥' if success else '💩'   )
    if not success:
        raise Exception('Something went wRoNg'   )

    hf_wavavec.save_pretrained(_lowercase   )

    if is_finetuned:
        processor.save_pretrained(_lowercase   )
    else:
        processor.feature_extractor.save_pretrained(_lowercase   )


if __name__ == "__main__":
   lowerCamelCase					    =	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(
       '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   lowerCamelCase					    =	parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )
