





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Union[str, Any]							  =   """wavlm"""





     def __init__(     self				,							_UpperCamelCase=32				,							_UpperCamelCase=768				,							_UpperCamelCase=12				,							_UpperCamelCase=12				,							_UpperCamelCase=3072				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-5				,							_UpperCamelCase="group"				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)				,							_UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)				,							_UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)				,							_UpperCamelCase=False				,							_UpperCamelCase=128				,							_UpperCamelCase=16				,							_UpperCamelCase=320				,							_UpperCamelCase=800				,							_UpperCamelCase=False				,							_UpperCamelCase=True				,							_UpperCamelCase=0.05				,							_UpperCamelCase=10				,							_UpperCamelCase=2				,							_UpperCamelCase=0.0				,							_UpperCamelCase=10				,							_UpperCamelCase=320				,							_UpperCamelCase=2				,							_UpperCamelCase=0.1				,							_UpperCamelCase=100				,							_UpperCamelCase=256				,							_UpperCamelCase=256				,							_UpperCamelCase=0.1				,							_UpperCamelCase="mean"				,							_UpperCamelCase=False				,							_UpperCamelCase=False				,							_UpperCamelCase=256				,							_UpperCamelCase=(512, 512, 512, 512, 1500)				,							_UpperCamelCase=(5, 3, 3, 1, 1)				,							_UpperCamelCase=(1, 2, 3, 1, 1)				,							_UpperCamelCase=512				,							_UpperCamelCase=80				,							_UpperCamelCase=0				,							_UpperCamelCase=1				,							_UpperCamelCase=2				,							_UpperCamelCase=False				,							_UpperCamelCase=3				,							_UpperCamelCase=2				,							_UpperCamelCase=3				,							_UpperCamelCase=None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase				,							pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase					)
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   feat_extract_norm
        _UpperCAmelCase           =   feat_extract_activation
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   conv_bias
        _UpperCAmelCase           =   num_buckets
        _UpperCAmelCase           =   max_bucket_distance
        _UpperCAmelCase           =   num_conv_pos_embeddings
        _UpperCAmelCase           =   num_conv_pos_embedding_groups
        _UpperCAmelCase           =   len(self.conv_dim					)
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   hidden_dropout
        _UpperCAmelCase           =   attention_dropout
        _UpperCAmelCase           =   activation_dropout
        _UpperCAmelCase           =   feat_proj_dropout
        _UpperCAmelCase           =   final_dropout
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   num_ctc_classes
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   do_stable_layer_norm
        _UpperCAmelCase           =   use_weighted_layer_sum
        _UpperCAmelCase           =   classifier_proj_size

        if (
            (len(self.conv_stride					) != self.num_feat_extract_layers)
            or (len(self.conv_kernel					) != self.num_feat_extract_layers)
            or (len(self.conv_dim					) != self.num_feat_extract_layers)
        ):
           raise ValueError(
               '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
               ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
               f''' {len(self.conv_dim					)}`, `len(config.conv_stride) = {len(self.conv_stride					)}`,'''
               f''' `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'''					)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _UpperCAmelCase           =   apply_spec_augment
        _UpperCAmelCase           =   mask_time_prob
        _UpperCAmelCase           =   mask_time_length
        _UpperCAmelCase           =   mask_time_min_masks
        _UpperCAmelCase           =   mask_feature_prob
        _UpperCAmelCase           =   mask_feature_length

        # parameters for pretraining with codevector quantized representations
        _UpperCAmelCase           =   num_codevectors_per_group
        _UpperCAmelCase           =   num_codevector_groups
        _UpperCAmelCase           =   contrastive_logits_temperature
        _UpperCAmelCase           =   num_negatives
        _UpperCAmelCase           =   codevector_dim
        _UpperCAmelCase           =   proj_codevector_dim
        _UpperCAmelCase           =   diversity_loss_weight

        # ctc loss
        _UpperCAmelCase           =   ctc_loss_reduction
        _UpperCAmelCase           =   ctc_zero_infinity

        # adapter
        _UpperCAmelCase           =   add_adapter
        _UpperCAmelCase           =   adapter_kernel_size
        _UpperCAmelCase           =   adapter_stride
        _UpperCAmelCase           =   num_adapter_layers
        _UpperCAmelCase           =   output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _UpperCAmelCase           =   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   list(_UpperCamelCase					)
        _UpperCAmelCase           =   xvector_output_dim





     @property
     def 		UpperCamelCase(     self					):
        return functools.reduce(operator.mul				,							self.conv_stride				,							1					)






'''simple docstring'''






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, 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_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


