"""simple docstring"""




import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							ASTConfig()

							if "10-10" in model_name:
														pass
							elif "speech-commands" in model_name:
														A__	     =							128
							elif "12-12" in model_name:
														A__	     =							12
														A__	     =							12
							elif "14-14" in model_name:
														A__	     =							14
														A__	     =							14
							elif "16-16" in model_name:
														A__	     =							16
														A__	     =							16
							else:
														raise ValueError('Model not supported'				)

							A__	     =							'huggingface/label-files'
							if "speech-commands" in model_name:
														A__	     =							35
														A__	     =							'speech-commands-v2-id2label.json'
							else:
														A__	     =							527
														A__	     =							'audioset-id2label.json'

							A__	     =							json.load(open(hf_hub_download(UpperCamelCase__       ,    UpperCamelCase__       ,    repo_type='dataset'				)       ,    'r'				)				)
							A__	     =							{int(UpperCamelCase__				): v for k, v in idalabel.items()}
							A__	     =							idalabel
							A__	     =							{v: k for k, v in idalabel.items()}

							return config






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if "module.v" in name:
														A__	     =							name.replace('module.v'       ,    'audio_spectrogram_transformer'				)
							if "cls_token" in name:
														A__	     =							name.replace('cls_token'       ,    'embeddings.cls_token'				)
							if "dist_token" in name:
														A__	     =							name.replace('dist_token'       ,    'embeddings.distillation_token'				)
							if "pos_embed" in name:
														A__	     =							name.replace('pos_embed'       ,    'embeddings.position_embeddings'				)
							if "patch_embed.proj" in name:
														A__	     =							name.replace('patch_embed.proj'       ,    'embeddings.patch_embeddings.projection'				)
							# transformer blocks
							if "blocks" in name:
														A__	     =							name.replace('blocks'       ,    'encoder.layer'				)
							if "attn.proj" in name:
														A__	     =							name.replace('attn.proj'       ,    'attention.output.dense'				)
							if "attn" in name:
														A__	     =							name.replace('attn'       ,    'attention.self'				)
							if "norm1" in name:
														A__	     =							name.replace('norm1'       ,    'layernorm_before'				)
							if "norm2" in name:
														A__	     =							name.replace('norm2'       ,    'layernorm_after'				)
							if "mlp.fc1" in name:
														A__	     =							name.replace('mlp.fc1'       ,    'intermediate.dense'				)
							if "mlp.fc2" in name:
														A__	     =							name.replace('mlp.fc2'       ,    'output.dense'				)
							# final layernorm
							if "audio_spectrogram_transformer.norm" in name:
														A__	     =							name.replace('audio_spectrogram_transformer.norm'       ,    'audio_spectrogram_transformer.layernorm'				)
							# classifier head
							if "module.mlp_head.0" in name:
														A__	     =							name.replace('module.mlp_head.0'       ,    'classifier.layernorm'				)
							if "module.mlp_head.1" in name:
														A__	     =							name.replace('module.mlp_head.1'       ,    'classifier.dense'				)

							return name






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							for key in orig_state_dict.copy().keys():
														A__	     =							orig_state_dict.pop(UpperCamelCase__				)

														if "qkv" in key:
																					A__	     =							key.split('.'				)
																					A__	     =							int(key_split[3]				)
																					A__	     =							config.hidden_size
																					if "weight" in key:
																												A__	     =							val[:dim, :]
																												A__	     =							val[dim : dim * 2, :]
																												A__	     =							val[-dim:, :]
																					else:
																												A__	     =							val[:dim]
																												A__	     =							val[dim : dim * 2]
																												A__	     =							val[-dim:]
														else:
																					A__	     =							val

							return orig_state_dict






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[
							    'module.v.head.weight',
							    'module.v.head.bias',
							    'module.v.head_dist.weight',
							    'module.v.head_dist.bias',
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase__       ,    UpperCamelCase__				)






@torch.no_grad()
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=False				):






							"""simple docstring"""





							A__	     =							get_audio_spectrogram_transformer_config(UpperCamelCase__				)

							A__	     =							{
							    'ast-finetuned-audioset-10-10-0.4593': (
							        'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-10-10-0.450': (
							        'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-10-10-0.448': (
							        'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-10-10-0.448-v2': (
							        'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-12-12-0.447': (
							        'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-14-14-0.443': (
							        'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
							    ),
							    'ast-finetuned-audioset-16-16-0.442': (
							        'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
							    ),
							    'ast-finetuned-speech-commands-v2': (
							        'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
							    ),
							}

							# load original state_dict
							A__	     =							model_name_to_url[model_name]
							A__	     =							torch.hub.load_state_dict_from_url(UpperCamelCase__       ,    map_location='cpu'				)
							# remove some keys
							remove_keys(UpperCamelCase__				)
							# rename some keys
							A__	     =							convert_state_dict(UpperCamelCase__       ,    UpperCamelCase__				)

							# load 🤗 model
							A__	     =							ASTForAudioClassification(UpperCamelCase__				)
							model.eval()

							model.load_state_dict(UpperCamelCase__				)

							# verify outputs on dummy input
							# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
							A__	     =							-4.2_6_7_7_3_9_3 if 'speech-commands' not in model_name else -6.8_4_5_9_7_8
							A__	     =							4.5_6_8_9_9_7_4 if 'speech-commands' not in model_name else 5.5_6_5_4_5_2_6
							A__	     =							1_024 if 'speech-commands' not in model_name else 128
							A__	     =							ASTFeatureExtractor(mean=UpperCamelCase__       ,    std=UpperCamelCase__       ,    max_length=UpperCamelCase__				)

							if "speech-commands" in model_name:
														A__	     =							load_dataset('speech_commands'       ,    'v0.02'       ,    split='validation'				)
														A__	     =							dataset[0]['audio']['array']
							else:
														A__	     =							hf_hub_download(
														    repo_id='nielsr/audio-spectogram-transformer-checkpoint'       ,    filename='sample_audio.flac'       ,    repo_type='dataset'       ,    )

														A__     ,  A__	     =							torchaudio.load(UpperCamelCase__				)
														A__	     =							waveform.squeeze().numpy()

							A__	     =							feature_extractor(UpperCamelCase__       ,    sampling_rate=16_000       ,    return_tensors='pt'				)

							# forward pass
							A__	     =							model(**UpperCamelCase__				)
							A__	     =							outputs.logits

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														A__	     =							torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]				)
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														A__	     =							torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]				)
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														A__	     =							torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]				)
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														A__	     =							torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]				)
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														A__	     =							torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]				)
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														A__	     =							torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]				)
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														A__	     =							torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]				)
							elif model_name == "ast-finetuned-speech-commands-v2":
														A__	     =							torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]				)
							else:
														raise ValueError('Unknown model name'				)
							if not torch.allclose(logits[0, :3]       ,    UpperCamelCase__       ,    atol=1E-4				):
														raise ValueError('Logits don\'t match'				)
							print('Looks ok!'				)

							if pytorch_dump_folder_path is not None:
														Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
														print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
														model.save_pretrained(UpperCamelCase__				)
														print(F'''Saving feature extractor to {pytorch_dump_folder_path}'''				)
														feature_extractor.save_pretrained(UpperCamelCase__				)

							if push_to_hub:
														print('Pushing model and feature extractor to the hub...'				)
														model.push_to_hub(F'''MIT/{model_name}'''				)
														feature_extractor.push_to_hub(F'''MIT/{model_name}'''				)


if __name__ == "__main__":
				__lowerCamelCase											=  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="ast-finetuned-audioset-10-10-0.4593",
				    type=str,
				    help="Name of the Audio Spectrogram Transformer model you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				__lowerCamelCase											=  parser.parse_args()
				convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf
class     UpperCamelCase__(			enum.Enum ):
						lowerCAmelCase__  :						Optional[Any]													=		0
						lowerCAmelCase__  :						Optional[int]													=		1
						lowerCAmelCase__  :						List[Any]													=		2






@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[Any]													=		'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Any:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													self.check_model_type(
													    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING     )
													if "prefix" not in self._preprocess_params:
																				# This is very specific. The logic is quite complex and needs to be done
																				# as a "default".
																				# It also defines both some preprocess_kwargs and generate_kwargs
																				# which is why we cannot put them in their respective methods.
																				A__	     =							None
																				if self.model.config.prefix is not None:
																											A__	     =							self.model.config.prefix
																				if prefix is None and self.model.__class__.__name__ in [
																				    "XLNetLMHeadModel",
																				    "TransfoXLLMHeadModel",
																				    "TFXLNetLMHeadModel",
																				    "TFTransfoXLLMHeadModel",
																				]:
																											# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																											A__	     =							self.XL_PREFIX
																				if prefix is not None:
																											# Recalculate some generate_kwargs linked to prefix.
																											A__     ,  A__     ,  A__	     =							self._sanitize_parameters(prefix=__UpperCAmelCase		,**self._forward_params     )
																											A__	     =							{**self._preprocess_params, **preprocess_params}
																											A__	     =							{**self._forward_params, **forward_params}





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase		,)					->				Dict:
													A__	     =							{}
													if prefix is not None:
																				A__	     =							prefix
													if prefix:
																				A__	     =							self.tokenizer(
																				    __UpperCAmelCase		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
																				A__	     =							prefix_inputs['input_ids'].shape[-1]

													if handle_long_generation is not None:
																				if handle_long_generation not in {"hole"}:
																											raise ValueError(
																											    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
																											    ' [None, \'hole\']'     )
																				A__	     =							handle_long_generation

													preprocess_params.update(__UpperCAmelCase     )
													A__	     =							generate_kwargs

													A__	     =							{}
													if return_full_text is not None and return_type is None:
																				if return_text is not None:
																											raise ValueError('`return_text` is mutually exclusive with `return_full_text`'     )
																				if return_tensors is not None:
																											raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'     )
																				A__	     =							ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
													if return_tensors is not None and return_type is None:
																				if return_text is not None:
																											raise ValueError('`return_text` is mutually exclusive with `return_tensors`'     )
																				A__	     =							ReturnType.TENSORS
													if return_type is not None:
																				A__	     =							return_type
													if clean_up_tokenization_spaces is not None:
																				A__	     =							clean_up_tokenization_spaces

													if stop_sequence is not None:
																				A__	     =							self.tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																				if len(__UpperCAmelCase     ) > 1:
																											warnings.warn(
																											    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																											    ' the stop sequence will be used as the stop sequence string in the interim.'     )
																				A__	     =							stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Dict:
													# Parse arguments
													if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																				kwargs.update({'add_space_before_punct_symbol': True}     )

													return super()._parse_and_tokenize(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Dict:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=""		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Dict:
													A__	     =							self.tokenizer(
													    prefix + prompt_text		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
													A__	     =							prompt_text

													if handle_long_generation == "hole":
																				A__	     =							inputs['input_ids'].shape[-1]
																				if "max_new_tokens" in generate_kwargs:
																											A__	     =							generate_kwargs['max_new_tokens']
																				else:
																											A__	     =							generate_kwargs.get('max_length'		,self.model.config.max_length     ) - cur_len
																											if new_tokens < 0:
																																		raise ValueError('We cannot infer how many new tokens are expected'     )
																				if cur_len + new_tokens > self.tokenizer.model_max_length:
																											A__	     =							self.tokenizer.model_max_length - new_tokens
																											if keep_length <= 0:
																																		raise ValueError(
																																		    'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
																																		    ' models max length'     )

																											A__	     =							inputs['input_ids'][:, -keep_length:]
																											if "attention_mask" in inputs:
																																		A__	     =							inputs['attention_mask'][:, -keep_length:]

													return inputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							model_inputs['input_ids']
													A__	     =							model_inputs.get('attention_mask'		,__UpperCAmelCase     )
													# Allow empty prompts
													if input_ids.shape[1] == 0:
																				A__	     =							None
																				A__	     =							None
																				A__	     =							1
													else:
																				A__	     =							input_ids.shape[0]
													A__	     =							model_inputs.pop('prompt_text'     )

													# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
													# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
													A__	     =							generate_kwargs.pop('prefix_length'		,0     )
													if prefix_length > 0:
																				A__	     =							'max_new_tokens' in generate_kwargs or (
																				    'generation_config' in generate_kwargs
																				    and generate_kwargs['generation_config'].max_new_tokens is not None
																				)
																				if not has_max_new_tokens:
																											A__	     =							generate_kwargs.get('max_length'     ) or self.model.config.max_length
																											generate_kwargs["max_length"] += prefix_length
																				A__	     =							'min_new_tokens' in generate_kwargs or (
																				    'generation_config' in generate_kwargs
																				    and generate_kwargs['generation_config'].min_new_tokens is not None
																				)
																				if not has_min_new_tokens and "min_length" in generate_kwargs:
																											generate_kwargs["min_length"] += prefix_length

        # BS x SL
													A__	     =							self.model.generate(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							generated_sequence.shape[0]
													if self.framework == "pt":
																				A__	     =							generated_sequence.reshape(__UpperCAmelCase		,out_b // in_b		,*generated_sequence.shape[1:]     )
													elif self.framework == "tf":
																				A__	     =							tf.reshape(__UpperCAmelCase		,(in_b, out_b // in_b, *generated_sequence.shape[1:])     )
													return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=ReturnType.FULL_TEXT		,__UpperCAmelCase=True     )					->				str:
													A__	     =							model_outputs['generated_sequence'][0]
													A__	     =							model_outputs['input_ids']
													A__	     =							model_outputs['prompt_text']
													A__	     =							generated_sequence.numpy().tolist()
													A__	     =							[]
													for sequence in generated_sequence:
																				if return_type == ReturnType.TENSORS:
																											A__	     =							{'generated_token_ids': sequence}
																				elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																											# Decode text
																											A__	     =							self.tokenizer.decode(
																											    __UpperCAmelCase		,skip_special_tokens=__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase		,)

																											# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																											if input_ids is None:
																																		A__	     =							0
																											else:
																																		A__	     =							len(
																																		    self.tokenizer.decode(
																																		        input_ids[0]		,skip_special_tokens=__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase		,)     )

																											if return_type == ReturnType.FULL_TEXT:
																																		A__	     =							prompt_text + text[prompt_length:]
																											else:
																																		A__	     =							text[prompt_length:]

																											A__	     =							{'generated_text': all_text}
																				records.append(__UpperCAmelCase     )

													return records





