


"""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()
__snake_case         =					logging.get_logger(__name__)







def 						__lowerCAmelCase		(		lowercase						:						List[Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   List[Any]         =					ASTConfig()

    if "10-10" in model_name:
        pass
    elif "speech-commands" in model_name:
        snake_case					:   Union[str, Any]         =					128
    elif "12-12" in model_name:
        snake_case					:   Tuple         =					12
        snake_case					:   Union[str, Any]         =					12
    elif "14-14" in model_name:
        snake_case					:   str         =					14
        snake_case					:   Dict         =					14
    elif "16-16" in model_name:
        snake_case					:   List[str]         =					16
        snake_case					:   Optional[Any]         =					16
    else:
        raise ValueError("Model not supported"				)

    snake_case					:   Dict         =					"huggingface/label-files"
    if "speech-commands" in model_name:
        snake_case					:   int         =					35
        snake_case					:   int         =					"speech-commands-v2-id2label.json"
    else:
        snake_case					:   str         =					527
        snake_case					:   Tuple         =					"audioset-id2label.json"

    snake_case					:   str         =					json.load(open(hf_hub_download(lowercase		,							lowercase		,							repo_type="dataset"				)		,							"r"				)				)
    snake_case					:   Tuple         =					{int(lowercase				): v for k, v in idalabel.items()}
    snake_case					:   Dict         =					idalabel
    snake_case					:   Dict         =					{v: k for k, v in idalabel.items()}

    return config







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		Union[str, Any]:


    """simple docstring"""





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

    return name







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						str				)			->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case					:   str         =					orig_state_dict.pop(lowercase				)

        if "qkv" in key:
            snake_case					:   str         =					key.split("."				)
            snake_case					:   Union[str, Any]         =					int(key_split[3]				)
            snake_case					:   List[Any]         =					config.hidden_size
            if "weight" in key:
                snake_case					:   Optional[Any]         =					val[:dim, :]
                snake_case					:   Dict         =					val[dim : dim * 2, :]
                snake_case					:   List[Any]         =					val[-dim:, :]
            else:
                snake_case					:   Tuple         =					val[:dim]
                snake_case					:   Union[str, Any]         =					val[dim : dim * 2]
                snake_case					:   Tuple         =					val[-dim:]
        else:
            snake_case					:   int         =					val

    return orig_state_dict







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					[
        "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(lowercase		,							lowercase				)







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						Dict		,							lowercase						:						Union[str, Any]=False				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					get_audio_spectrogram_transformer_config(lowercase				)

    snake_case					:   int         =					{
        "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
    snake_case					:   Tuple         =					model_name_to_url[model_name]
    snake_case					:   Optional[Any]         =					torch.hub.load_state_dict_from_url(lowercase		,							map_location="cpu"				)
    # remove some keys
    remove_keys(lowercase				)
    # rename some keys
    snake_case					:   List[Any]         =					convert_state_dict(lowercase		,							lowercase				)

    # load 🤗 model
    snake_case					:   Dict         =					ASTForAudioClassification(lowercase				)
    model.eval()

    model.load_state_dict(lowercase				)

    # verify outputs on dummy input
    # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
    snake_case					:   Dict         =					-4.267_7393 if "speech-commands" not in model_name else -6.84_5978
    snake_case					:   Any         =					4.568_9974 if "speech-commands" not in model_name else 5.565_4526
    snake_case					:   Union[str, Any]         =					1024 if "speech-commands" not in model_name else 128
    snake_case					:   List[Any]         =					ASTFeatureExtractor(mean=lowercase		,							std=lowercase		,							max_length=lowercase				)

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

        snake_case					:   Tuple         =					torchaudio.load(lowercase				)
        snake_case					:   Union[str, Any]         =					waveform.squeeze().numpy()

    snake_case					:   Tuple         =					feature_extractor(lowercase		,							sampling_rate=1_6000		,							return_tensors="pt"				)

    # forward pass
    snake_case					:   Any         =					model(**lowercase				)
    snake_case					:   Tuple         =					outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        snake_case					:   Optional[int]         =					torch.tensor([-0.8760, -7.0042, -8.6602]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        snake_case					:   List[str]         =					torch.tensor([-1.1986, -7.0903, -8.2718]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        snake_case					:   Tuple         =					torch.tensor([-2.6128, -8.0080, -9.4344]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        snake_case					:   Optional[int]         =					torch.tensor([-1.5080, -7.4534, -8.8917]				)
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        snake_case					:   int         =					torch.tensor([-0.5050, -6.5833, -8.0843]				)
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        snake_case					:   List[str]         =					torch.tensor([-0.3826, -7.0336, -8.2413]				)
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        snake_case					:   Optional[Any]         =					torch.tensor([-1.2113, -6.9101, -8.3470]				)
    elif model_name == "ast-finetuned-speech-commands-v2":
        snake_case					:   Dict         =					torch.tensor([6.1589, -8.0566, -8.7984]				)
    else:
        raise ValueError("Unknown model name"				)
    if not torch.allclose(logits[0, :3]		,							lowercase		,							atol=1e-4				):
        raise ValueError("Logits don't match"				)
    print("Looks ok!"				)

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

    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__":
  __snake_case         =					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."""
  )

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









"""simple docstring"""



import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case         =					"""src/diffusers"""
__snake_case         =					"""."""


# This is to make sure the diffusers module imported is the one in the repo.
__snake_case         =					importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__snake_case         =					spec.loader.load_module()







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Optional[int]				)			->		Union[str, Any]:


    """simple docstring"""





    return line.startswith(lowercase				) or len(lowercase				) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"		,							lowercase				) is not None







def 						__lowerCAmelCase		(		lowercase						:						List[Any]				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   Any         =					object_name.split("."				)
    snake_case					:   Dict         =					0

    # First let's find the module where our object lives.
    snake_case					:   Optional[Any]         =					parts[i]
    while i < len(lowercase				) and not os.path.isfile(os.path.join(lowercase		,							F'{module}.py'				)				):
        i += 1
        if i < len(lowercase				):
            snake_case					:   List[Any]         =					os.path.join(lowercase		,							parts[i]				)
    if i >= len(lowercase				):
        raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'				)

    with open(os.path.join(lowercase		,							F'{module}.py'				)		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
        snake_case					:   str         =					f.readlines()

    # Now let's find the class / func in the code!
    snake_case					:   List[str]         =					""
    snake_case					:   Optional[Any]         =					0
    for name in parts[i + 1 :]:
        while (
            line_index < len(lowercase				) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'		,							lines[line_index]				) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(lowercase				):
        raise ValueError(F' {object_name} does not match any function or class in {module}.'				)

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    snake_case					:   Union[str, Any]         =					line_index
    while line_index < len(lowercase				) and _should_continue(lines[line_index]		,							lowercase				):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]				) <= 1:
        line_index -= 1

    snake_case					:   Tuple         =					lines[start_index:line_index]
    return "".join(lowercase				)


__snake_case         =					re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
__snake_case         =					re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
__snake_case         =					re.compile(R"""<FILL\s+[^>]*>""")







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		Dict:


    """simple docstring"""





    snake_case					:   List[str]         =					code.split("\n"				)
    snake_case					:   Tuple         =					0
    while idx < len(lowercase				) and len(lines[idx]				) == 0:
        idx += 1
    if idx < len(lowercase				):
        return re.search(R"^(\s*)\S"		,							lines[idx]				).groups()[0]
    return ""







def 						__lowerCAmelCase		(		lowercase						:						Any				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					len(get_indent(lowercase				)				) > 0
    if has_indent:
        snake_case					:   List[Any]         =					F'class Bla:\n{code}'
    snake_case					:   List[Any]         =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119		,							preview=lowercase				)
    snake_case					:   str         =					black.format_str(lowercase		,							mode=lowercase				)
    snake_case      ,snake_case					:   int         =					style_docstrings_in_code(lowercase				)
    return result[len("class Bla:\n"				) :] if has_indent else result







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						List[Any]=False				)			->		Dict:


    """simple docstring"""





    with open(lowercase		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
        snake_case					:   str         =					f.readlines()
    snake_case					:   Optional[int]         =					[]
    snake_case					:   List[Any]         =					0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(lowercase				):
        snake_case					:   List[Any]         =					_re_copy_warning.search(lines[line_index]				)
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        snake_case      ,snake_case      ,snake_case					:   List[str]         =					search.groups()
        snake_case					:   List[Any]         =					find_code_in_diffusers(lowercase				)
        snake_case					:   Dict         =					get_indent(lowercase				)

        snake_case					:   Union[str, Any]         =					line_index + 1 if indent == theoretical_indent else line_index + 2
        snake_case					:   Any         =					theoretical_indent
        snake_case					:   Optional[int]         =					start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        snake_case					:   Dict         =					True
        while line_index < len(lowercase				) and should_continue:
            line_index += 1
            if line_index >= len(lowercase				):
                break
            snake_case					:   int         =					lines[line_index]
            snake_case					:   Tuple         =					_should_continue(lowercase		,							lowercase				) and re.search(F'^{indent}# End copy'		,							lowercase				) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]				) <= 1:
            line_index -= 1

        snake_case					:   Optional[int]         =					lines[start_index:line_index]
        snake_case					:   List[Any]         =					"".join(lowercase				)

        # Remove any nested `Copied from` comments to avoid circular copies
        snake_case					:   List[str]         =					[line for line in theoretical_code.split("\n"				) if _re_copy_warning.search(lowercase				) is None]
        snake_case					:   Optional[int]         =					"\n".join(lowercase				)

        # Before comparing, use the `replace_pattern` on the original code.
        if len(lowercase				) > 0:
            snake_case					:   List[str]         =					replace_pattern.replace("with"		,							""				).split(","				)
            snake_case					:   Union[str, Any]         =					[_re_replace_pattern.search(lowercase				) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                snake_case      ,snake_case      ,snake_case					:   str         =					pattern.groups()
                snake_case					:   Any         =					re.sub(lowercase		,							lowercase		,							lowercase				)
                if option.strip() == "all-casing":
                    snake_case					:   int         =					re.sub(obja.lower()		,							obja.lower()		,							lowercase				)
                    snake_case					:   Optional[int]         =					re.sub(obja.upper()		,							obja.upper()		,							lowercase				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            snake_case					:   Tuple         =					blackify(lines[start_index - 1] + theoretical_code				)
            snake_case					:   Any         =					theoretical_code[len(lines[start_index - 1]				) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]				)
            if overwrite:
                snake_case					:   Any         =					lines[:start_index] + [theoretical_code] + lines[line_index:]
                snake_case					:   Tuple         =					start_index + 1

    if overwrite and len(lowercase				) > 0:
        # Warn the user a file has been modified.
        print(F'Detected changes, rewriting {filename}.'				)
        with open(lowercase		,							"w"		,							encoding="utf-8"		,							newline="\n"				) as f:
            f.writelines(lowercase				)
    return diffs







def 						__lowerCAmelCase		(		lowercase						:						bool = False				)			->		Optional[Any]:


    """simple docstring"""





    snake_case					:   str         =					glob.glob(os.path.join(lowercase		,							"**/*.py"				)		,							recursive=lowercase				)
    snake_case					:   List[str]         =					[]
    for filename in all_files:
        snake_case					:   List[str]         =					is_copy_consistent(lowercase		,							lowercase				)
        diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(lowercase				) > 0:
        snake_case					:   List[str]         =					"\n".join(lowercase				)
        raise Exception(
            "Found the following copy inconsistencies:\n"
            + diff
            + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."				)


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

  check_copies(args.fix_and_overwrite)





