



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()
UpperCAmelCase__       =	logging.get_logger(__name__)





def 							A     (					_UpperCAmelCase    :       Dict   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       ASTConfig()

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

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

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

			return config





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			str:







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

			return name





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Tuple   )    ->			Optional[Any]:







			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						_UpperCAmelCase			=       orig_state_dict.pop(_UpperCAmelCase   )

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

			return orig_state_dict





def 							A     (					_UpperCAmelCase    :       Any   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       [
			    '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 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Any=False   )    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       get_audio_spectrogram_transformer_config(_UpperCAmelCase   )

			_UpperCAmelCase			=       {
			    '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
			_UpperCAmelCase			=       model_name_to_url[model_name]
			_UpperCAmelCase			=       torch.hub.load_state_dict_from_url(_UpperCAmelCase							,       map_location='cpu'   )
			# remove some keys
			remove_keys(_UpperCAmelCase   )
			# rename some keys
			_UpperCAmelCase			=       convert_state_dict(_UpperCAmelCase							,       _UpperCAmelCase   )

			# load 🤗 model
			_UpperCAmelCase			=       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
			_UpperCAmelCase			=       -4.2677393 if 'speech-commands' not in model_name else -6.845978
			_UpperCAmelCase			=       4.5689974 if 'speech-commands' not in model_name else 5.5654526
			_UpperCAmelCase			=       1_024 if 'speech-commands' not in model_name else 128
			_UpperCAmelCase			=       ASTFeatureExtractor(mean=_UpperCAmelCase							,       std=_UpperCAmelCase							,       max_length=_UpperCAmelCase   )

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       torchaudio.load(_UpperCAmelCase   )
						_UpperCAmelCase			=       waveform.squeeze().numpy()

			_UpperCAmelCase			=       feature_extractor(_UpperCAmelCase							,       sampling_rate=16_000							,       return_tensors='pt'   )

			# forward pass
			_UpperCAmelCase			=       model(**_UpperCAmelCase   )
			_UpperCAmelCase			=       outputs.logits

			if model_name == "ast-finetuned-audioset-10-10-0.4593":
						_UpperCAmelCase			=       torch.tensor([-0.8760, -7.0042, -8.6602]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.450":
						_UpperCAmelCase			=       torch.tensor([-1.1986, -7.0903, -8.2718]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.448":
						_UpperCAmelCase			=       torch.tensor([-2.6128, -8.0080, -9.4344]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
						_UpperCAmelCase			=       torch.tensor([-1.5080, -7.4534, -8.8917]   )
			elif model_name == "ast-finetuned-audioset-12-12-0.447":
						_UpperCAmelCase			=       torch.tensor([-0.5050, -6.5833, -8.0843]   )
			elif model_name == "ast-finetuned-audioset-14-14-0.443":
						_UpperCAmelCase			=       torch.tensor([-0.3826, -7.0336, -8.2413]   )
			elif model_name == "ast-finetuned-audioset-16-16-0.442":
						_UpperCAmelCase			=       torch.tensor([-1.2113, -6.9101, -8.3470]   )
			elif model_name == "ast-finetuned-speech-commands-v2":
						_UpperCAmelCase			=       torch.tensor([6.1589, -8.0566, -8.7984]   )
			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__":
							UpperCAmelCase__       =	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."
							)

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





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







			'''simple docstring'''
			with open(_UpperCAmelCase							,       'r'							,       encoding='utf-8'							,       newline='\n'   ) as f:
						_UpperCAmelCase			=       f.readlines()
			# Find the start prompt.
			_UpperCAmelCase			=       0
			while not lines[start_index].startswith(_UpperCAmelCase   ):
						start_index += 1
			start_index += 1

			_UpperCAmelCase			=       start_index
			while not lines[end_index].startswith(_UpperCAmelCase   ):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]   ) <= 1:
						start_index += 1
			while len(lines[end_index]   ) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__       =	direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__       =	{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCAmelCase__       =	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       set()   )
			_UpperCAmelCase			=       {
			    code: name
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if (code in model_maping_names or code in special_model_types)
			}
			return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()]   ) + "\n"





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,       end_prompt='<!--End of the generated tip-->'							,       )

			_UpperCAmelCase			=       get_model_list_for_task(_UpperCAmelCase   )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       'w'							,       encoding='utf-8'							,       newline='\n'   ) as f:
												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
						else:
									raise ValueError(
									    F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
									    ' to fix this.'   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__       =	parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)
