
from string import ascii_uppercase




def 						A_			(				snake_case				: str			,   snake_case				: str			,   snake_case				: bool = False			,   snake_case				: str = ascii_uppercase			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      keyword.upper()
						__UpperCamelCase  	=      plaintext.upper()
						__UpperCamelCase  	=      set(snake_case			)

						# create a list of unique characters in the keyword - their order matters
						# it determines how we will map plaintext characters to the ciphertext
						__UpperCamelCase  	=      []
						for char in keyword:
												if char in alphabet_set and char not in unique_chars:
																		unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
						__UpperCamelCase  	=      len(snake_case			)

						# create a shifted version of the alphabet
						__UpperCamelCase  	=      unique_chars + [
						    char for char in alphabet if char not in unique_chars
						]

						# create a modified alphabet by splitting the shifted alphabet into rows
						__UpperCamelCase  	=      [
						    shifted_alphabet[k : k + num_unique_chars_in_keyword]
						    for k in range(0			,   26			,   snake_case			)
						]

						# map the alphabet characters to the modified alphabet characters
						# going 'vertically' through the modified alphabet - consider columns first
						__UpperCamelCase  	=      {}
						__UpperCamelCase  	=      0
						for column in range(snake_case			):
												for row in modified_alphabet:
																		# if current row (the last one) is too short, break out of loop
																		if len(snake_case			) <= column:
																								break

																		# map current letter to letter in modified alphabet
																		__UpperCamelCase  	=      row[column]
																		letter_index += 1

						if verbose:
												print(snake_case			)
						# create the encrypted text by mapping the plaintext to the modified alphabet
						return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
				# example use
				print(mixed_keyword("college", "UNIVERSITY"))






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()
_A							=       logging.get_logger(__name__)

_A							=       {
    '''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''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

       if weight_type is not None:
              UpperCamelCase_											=							getattr(a__						,   a__  ).shape
       else:
              UpperCamelCase_											=							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":
              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 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

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

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

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""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]:
                                   UpperCamelCase_											=							True
                                   if "*" in mapped_key:
                                          UpperCamelCase_											=							name.split(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   if "weight_g" in name:
                                          UpperCamelCase_											=							"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCamelCase_											=							"""weight_v"""
                                   elif "bias" in name:
                                          UpperCamelCase_											=							"""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   else:
                                          UpperCamelCase_											=							None
                                   set_recursively(a__						,   a__						,   a__						,   a__						,   a__  )
                            continue
              if not is_used:
                     unused_weights.append(a__  )

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




def 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							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.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

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

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

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

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

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

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

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

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

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

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

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

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

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

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   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(a__  )

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


if __name__ == "__main__":
      _A							=       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'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



