



"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__a           =							logging.get_logger(__name__)
@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     List[nn.Module]		=	field(default_factory=_lowerCAmelCase							)
					_A						:     list		=	field(default_factory=_lowerCAmelCase							)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Union[str, Any]		,	snake_case:				Tensor		,	snake_case:				Tensor		)  ->      List[str]:
											snake_case_					:Optional[int]		=     len(list(m.modules()		)		) == 1 or isinstance(snake_case		,	nn.Convad		) or isinstance(snake_case		,	nn.BatchNormad		)
											if has_not_submodules:
																	if not isinstance(snake_case		,	snake_case		):
																							self.traced.append(snake_case		)






					def __call__(			self:				Optional[Any]		,	snake_case:				Tensor		)  ->      Optional[Any]:
											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook		)		)
											self.module(snake_case		)
											[x.remove() for x in self.handles]
											return self






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case		: len(list(x.state_dict().keys()		)		) > 0		,	self.traced		)		)

@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     nn.Module
					_A						:     int		=	0
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)






					def __call__(			self:				List[Any]		,	snake_case:				Tensor		)  ->      List[Any]:
											snake_case_					:Optional[int]		=     Tracker(self.dest		)(snake_case		).parametrized
											snake_case_					:int		=     Tracker(self.src		)(snake_case		).parametrized

											snake_case_					:Optional[Any]		=     list(filter(lambda snake_case		: type(snake_case		) not in self.src_skip		,	snake_case		)		)
											snake_case_					:Any		=     list(filter(lambda snake_case		: type(snake_case		) not in self.dest_skip		,	snake_case		)		)

											if len(snake_case		) != len(snake_case		):
																	raise Exception(
																	    f"""Numbers of operations are different. Source module has {len(snake_case		)} operations while"""
																	    f""" destination module has {len(snake_case		)}."""		)

											for dest_m, src_m in zip(snake_case		,	snake_case		):
																	dest_m.load_state_dict(src_m.state_dict()		)
																	if self.verbose == 1:
																							print(f"""Transfered from={src_m} to={dest_m}"""		)

def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     from_model.state_dict()
						snake_case_					:Optional[Any]		=     our_model.state_dict()
						snake_case_					:Dict		=     our_model.config
						snake_case_					:List[str]		=     []
						for stage_idx in range(len(config.hidden_sizes      )      ):
												for block_id in range(config.depths[stage_idx]      ):
																		snake_case_					:List[Any]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		snake_case_					:Dict		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)      )
																		snake_case_					:Optional[int]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		snake_case_					:Tuple		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

																		all_keys.append((from_key, to_key)      )

						for from_key, to_key in all_keys:
												snake_case_					:List[str]		=     from_state_dict.pop(_lowercase      )

						our_model.load_state_dict(_lowercase      )
						return our_model
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase = True,   ):







						'''simple docstring'''


						print(f"""Downloading weights for {name}..."""      )
						snake_case_					:Union[str, Any]		=     cached_download(_lowercase      )
						print(f"""Converting {name}..."""      )
						snake_case_					:Union[str, Any]		=     torch.load(_lowercase      )["""state_dict"""]
						from_model.load_state_dict(_lowercase      )
						from_model.eval()
						with torch.no_grad():
												snake_case_					:List[str]		=     VanForImageClassification(_lowercase      ).eval()
												snake_case_					:Tuple		=     ModuleTransfer(src=_lowercase,   dest=_lowercase      )
												snake_case_					:int		=     torch.randn((1, 3, 224, 224)      )
												module_transfer(_lowercase      )
												snake_case_					:int		=     copy_parameters(_lowercase,   _lowercase      )

						if not torch.allclose(from_model(_lowercase      ),   our_model(_lowercase      ).logits      ):
												raise ValueError("""The model logits don't match the original one."""      )

						snake_case_					:List[str]		=     name
						print(_lowercase      )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,   commit_message="""Add model""",   use_temp_dir=_lowercase,   )

												# we can use the convnext one
												snake_case_					:str		=     AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,   commit_message="""Add image processor""",   use_temp_dir=_lowercase,   )

												print(f"""Pushed {checkpoint_name}"""      )
def   A_    (     _lowercase,   _lowercase = None,   _lowercase = True      ):







						'''simple docstring'''


						snake_case_					:str		=     """imagenet-1k-id2label.json"""
						snake_case_					:int		=     1000

						snake_case_					:List[Any]		=     """huggingface/label-files"""
						snake_case_					:List[Any]		=     num_labels
						snake_case_					:Optional[Any]		=     json.load(open(hf_hub_download(_lowercase,   _lowercase,   repo_type="""dataset"""      ),   """r"""      )      )
						snake_case_					:int		=     {int(_lowercase      ): v for k, v in idalabel.items()}

						snake_case_					:str		=     idalabel
						snake_case_					:List[str]		=     {v: k for k, v in idalabel.items()}

						snake_case_					:str		=     partial(_lowercase,   num_labels=_lowercase,   idalabel=_lowercase,   labelaid=_lowercase      )

						snake_case_					:Dict		=     {
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256],   depths=[3, 3, 5, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[2, 2, 4, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 3, 12, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 5, 27, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						}

						snake_case_					:Optional[int]		=     {
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						snake_case_					:str		=     {
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    _lowercase,   names_to_config[model_name],   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _lowercase,   _lowercase,   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )


if __name__ == "__main__":
						__a           =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model-name",
						    default=None,
						    type=str,
						    help=(
						        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
						        " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=Path,
						    required=True,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--van_dir",
						    required=True,
						    type=Path,
						    help=(
						        "A path to VAN's original implementation directory. You can download from here:"
						        " https://github.com/Visual-Attention-Network/VAN-Classification"
						    ),
						)
						parser.add_argument(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						__a           =							parser.parse_args()
						__a           =							args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						__a           =							args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


lowerCamelCase_					  =   random.Random()



def   UpperCamelCase(   lowercase_     ,   lowercase_=1.0     ,   lowercase_=None     ,   lowercase_=None				)      ->							List[str]:





  '''simple docstring'''





  if rng is None:
    snake_case_					= global_rng

  snake_case_					= []
  for batch_idx in range(shape[0]				):
    values.append([]				)
    for _ in range(shape[1]				):
      values[-1].append(rng.random() * scale				)

  return values







@require_torch
@require_sentencepiece
class 					__lowerCamelCase							(      unittest.TestCase      ):







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/musicgen-small"""
        snake_case_					= tempfile.mkdtemp()







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			int:
        return TaTokenizer.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Dict:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case_					= MusicgenProcessor.from_pretrained(self.tmpdirname  )

        self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()  )
        self.assertIsInstance(processor.tokenizer				,    lowerCamelCase  )

        self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()  )
        self.assertIsInstance(processor.feature_extractor				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= MusicgenProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

        snake_case_					= self.get_tokenizer(bos_token="""(BOS)"""				,    eos_token="""(EOS)"""  )
        snake_case_					= self.get_feature_extractor(do_normalize=lowerCamelCase				,    padding_value=1.0  )

        snake_case_					= MusicgenProcessor.from_pretrained(
            self.tmpdirname				,    bos_token="""(BOS)"""				,    eos_token="""(EOS)"""				,    do_normalize=lowerCamelCase				,    padding_value=1.0  )

        self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()  )
        self.assertIsInstance(processor.tokenizer				,    lowerCamelCase  )

        self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()  )
        self.assertIsInstance(processor.feature_extractor				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= processor(lowerCamelCase				,    return_tensors="""np"""  )

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]  )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_					= processor.batch_decode(sequences=lowerCamelCase  )
        snake_case_					= tokenizer.batch_decode(lowerCamelCase  )

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        self.assertListEqual(
            processor.model_input_names				,    feature_extractor.model_input_names				,    msg="""`processor` and `feature_extractor` model input names do not match"""				,    )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.get_feature_extractor(padding_side="""left"""  )
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= [floats_list((1, x)  )[0] for x in range(5				,    20				,    5  )]
        snake_case_					= processor(lowerCamelCase  ).padding_mask

        snake_case_					= np.asarray(floats_list((3, 20)  )  )[:, None, :]
        snake_case_					= processor.batch_decode(lowerCamelCase				,    padding_mask=lowerCamelCase  )

        self.assertIsInstance(lowerCamelCase				,    lowerCamelCase  )

        for audio in decoded_audios:
          self.assertIsInstance(lowerCamelCase				,    np.ndarray  )

        self.assertTrue(decoded_audios[0].shape == (1, 10)  )
        self.assertTrue(decoded_audios[1].shape == (1, 15)  )
        self.assertTrue(decoded_audios[2].shape == (1, 20)  )