





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_features""", """is_longer"""]


      def __init__(		self   ,				a__=64   ,				a__=4_8000   ,				a__=480   ,				a__=10   ,				a__=1024   ,				a__=0.0   ,				a__=False   ,				a__ = 0   ,				a__ = 1_4000   ,				a__ = None   ,				a__ = "fusion"   ,				a__ = "repeatpad"   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(
                 feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				return_attention_mask=a__   ,				**a__   ,				)
             _lowerCamelCase				:  int						=		top_db
             _lowerCamelCase				:  List[str]						=		truncation
             _lowerCamelCase				:  Tuple						=		padding
             _lowerCamelCase				:  List[str]						=		fft_window_size
             _lowerCamelCase				:  int						=		(fft_window_size >> 1) + 1
             _lowerCamelCase				:  int						=		hop_length
             _lowerCamelCase				:  Any						=		max_length_s
             _lowerCamelCase				:  str						=		max_length_s * sampling_rate
             _lowerCamelCase				:  Union[str, Any]						=		sampling_rate
             _lowerCamelCase				:  int						=		frequency_min
             _lowerCamelCase				:  Dict						=		frequency_max
             _lowerCamelCase				:  Union[str, Any]						=		mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=a__   ,				min_frequency=a__   ,				max_frequency=a__   ,				sampling_rate=a__   ,				norm=a__   ,				mel_scale='''htk'''   ,				)
             _lowerCamelCase				:  Any						=		mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=a__   ,				min_frequency=a__   ,				max_frequency=a__   ,				sampling_rate=a__   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  Union[str, Any]						=		self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             if "mel_filters_slaney" in output:
                    del output["mel_filters_slaney"]
             return output


      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		spectrogram(
                 a__   ,				window_function(self.fft_window_size   ,				'''hann''')   ,				frame_length=self.fft_window_size   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=a__   ,				log_mel='''dB'''   ,				)
             return log_mel_spectrogram.T


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		np.array_split(list(range(0   ,				total_frames - chunk_frames + 1))   ,				3)
             if len(ranges[1]) == 0:
                    # if the audio is too short, we just use the first chunk
                    _lowerCamelCase				:  List[str]						=		[0]
             if len(ranges[2]) == 0:
                    # if the audio is too short, we just use the first chunk
                    _lowerCamelCase				:  Union[str, Any]						=		[0]
             # randomly choose index for each part
             _lowerCamelCase				:  List[str]						=		np.random.choice(ranges[0])
             _lowerCamelCase				:  Tuple						=		np.random.choice(ranges[1])
             _lowerCamelCase				:  str						=		np.random.choice(ranges[2])

             _lowerCamelCase				:  Union[str, Any]						=		mel[idx_front : idx_front + chunk_frames, :]
             _lowerCamelCase				:  Optional[Any]						=		mel[idx_middle : idx_middle + chunk_frames, :]
             _lowerCamelCase				:  Any						=		mel[idx_back : idx_back + chunk_frames, :]

             _lowerCamelCase				:  Tuple						=		torch.tensor(mel[None, None, :])
             _lowerCamelCase				:  List[Any]						=		torch.nn.functional.interpolate(
                 a__   ,				size=[chunk_frames, 64]   ,				mode='''bilinear'''   ,				align_corners=a__)
             _lowerCamelCase				:  Union[str, Any]						=		mel_shrink[0][0].numpy()
             _lowerCamelCase				:  str						=		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,				axis=0)
             return mel_fusion


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             if waveform.shape[0] > max_length:
                    if truncation == "rand_trunc":
                           _lowerCamelCase				:  Tuple						=		True
                           # random crop to max_length (for compatibility) -> this should be handled by self.pad
                           _lowerCamelCase				:  str						=		len(a__) - max_length
                           _lowerCamelCase				:  Dict						=		np.random.randint(0   ,				overflow + 1)
                           _lowerCamelCase				:  int						=		waveform[idx : idx + max_length]
                           _lowerCamelCase				:  List[Any]						=		self._np_extract_fbank_features(a__   ,				self.mel_filters_slaney)[None, :]
                    elif truncation == "fusion":
                           _lowerCamelCase				:  Any						=		self._np_extract_fbank_features(a__   ,				self.mel_filters)
                           _lowerCamelCase				:  List[Any]						=		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                           _lowerCamelCase				:  int						=		mel.shape[0]
                           if chunk_frames == total_frames:
                                  # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                                  # In this case, we just use the whole audio.
                                  _lowerCamelCase				:  int						=		np.stack([mel, mel, mel, mel]   ,				axis=0)
                                  _lowerCamelCase				:  int						=		False
                           else:
                                  _lowerCamelCase				:  Optional[int]						=		self._random_mel_fusion(a__   ,				a__   ,				a__)
                                  _lowerCamelCase				:  Optional[Any]						=		True
                    else:
                           raise NotImplementedError(F"""data_truncating {truncation} not implemented""")

             else:
                    _lowerCamelCase				:  List[Any]						=		False
                    # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                    if waveform.shape[0] < max_length:
                           if padding == "repeat":
                                  _lowerCamelCase				:  str						=		int(max_length / len(a__))
                                  _lowerCamelCase				:  List[str]						=		np.stack(np.tile(a__   ,				n_repeat + 1))[:max_length]
                           if padding == "repeatpad":
                                  _lowerCamelCase				:  Optional[int]						=		int(max_length / len(a__))
                                  _lowerCamelCase				:  Any						=		np.stack(np.tile(a__   ,				a__))
                           _lowerCamelCase				:  Optional[Any]						=		np.pad(a__   ,				(0, max_length - waveform.shape[0])   ,				mode='''constant'''   ,				constant_values=0)

                    if truncation == "fusion":
                           _lowerCamelCase				:  int						=		self._np_extract_fbank_features(a__   ,				self.mel_filters)
                           _lowerCamelCase				:  Optional[int]						=		np.stack([input_mel, input_mel, input_mel, input_mel]   ,				axis=0)
                    else:
                           _lowerCamelCase				:  List[str]						=		self._np_extract_fbank_features(a__   ,				self.mel_filters_slaney)[None, :]

             return input_mel, longer



      def __call__(		self   ,				a__   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		truncation if truncation is not None else self.truncation
             _lowerCamelCase				:  Optional[int]						=		padding if padding else self.padding

             if sampling_rate is not None:
                    if sampling_rate != self.sampling_rate:
                           raise ValueError(
                               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                               F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""")
             else:
                    logger.warning(
                        '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                        '''Failing to do so can result in silent errors that might be hard to debug.''')

             _lowerCamelCase				:  Any						=		isinstance(a__   ,				np.ndarray) and len(raw_speech.shape) > 1
             if is_batched_numpy and len(raw_speech.shape) > 2:
                    raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
             _lowerCamelCase				:  Optional[Any]						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)))
             )

             if is_batched:
                    _lowerCamelCase				:  Tuple						=		[np.asarray(a__   ,				dtype=np.floataa) for speech in raw_speech]
             elif not is_batched and not isinstance(a__   ,				np.ndarray):
                    _lowerCamelCase				:  Tuple						=		np.asarray(a__   ,				dtype=np.floataa)
             elif isinstance(a__   ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  Any						=		raw_speech.astype(np.floataa)

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  List[str]						=		[np.asarray(a__)]

             # convert to mel spectrogram, truncate and pad if needed.
             _lowerCamelCase				:  Any						=		[
                 self._get_input_mel(a__   ,				max_length if max_length else self.nb_max_samples   ,				a__   ,				a__)
                 for waveform in raw_speech
             ]

             _lowerCamelCase				:  str						=		[]
             _lowerCamelCase				:  List[str]						=		[]
             for mel, longer in padded_inputs:
                    input_mel.append(a__)
                    is_longer.append(a__)

             if truncation == "fusion" and sum(a__) == 0:
                    # if no audio is longer than 10s, then randomly select one audio to be longer
                    _lowerCamelCase				:  List[Any]						=		np.random.randint(0   ,				len(a__))
                    _lowerCamelCase				:  Tuple						=		True

             if isinstance(input_mel[0]   ,				a__):
                    _lowerCamelCase				:  Dict						=		[np.asarray(a__   ,				dtype=np.floataa) for feature in input_mel]

             # is_longer is a list of bool
             _lowerCamelCase				:  List[str]						=		[[longer] for longer in is_longer]

             _lowerCamelCase				:  Optional[int]						=		{'''input_features''': input_mel, '''is_longer''': is_longer}
             _lowerCamelCase				:  Tuple						=		BatchFeature(a__)

             if return_tensors is not None:
                    _lowerCamelCase				:  Any						=		input_features.convert_to_tensors(a__)

             return input_features








import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def  __UpperCAmelCase(   lowercase_   ):
       # vision encoder
       if "img_encoder.pos_embed" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''img_encoder.pos_embed'''			,     '''vision_model.embeddings.position_embeddings'''   )
       if "img_encoder.patch_embed.proj" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''img_encoder.patch_embed.proj'''			,     '''vision_model.embeddings.patch_embeddings.projection'''   )
       if "img_encoder.patch_embed.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.patch_embed.norm'''			,     '''vision_model.embeddings.layernorm'''   )
       if "img_encoder.layers" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.layers'''			,     '''vision_model.encoder.stages'''   )
       if "blocks" in name and "res" not in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''blocks'''			,     '''layers'''   )
       if "attn" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''attn'''			,     '''self_attn'''   )
       if "proj" in name and "self_attn" in name and "text" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''proj'''			,     '''out_proj'''   )
       if "pre_assign_attn.attn.proj" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''pre_assign_attn.attn.proj'''			,     '''pre_assign_attn.attn.out_proj'''   )
       if "norm1" in name:
              _lowerCamelCase				:  int						=		name.replace('''norm1'''			,     '''layer_norm1'''   )
       if "norm2" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''norm2'''			,     '''layer_norm2'''   )
       if "img_encoder.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.norm'''			,     '''vision_model.layernorm'''   )
       # text encoder
       if "text_encoder.token_embedding" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''text_encoder.token_embedding'''			,     '''text_model.embeddings.token_embedding'''   )
       if "text_encoder.positional_embedding" in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''text_encoder.positional_embedding'''			,     '''text_model.embeddings.position_embedding.weight'''   )
       if "text_encoder.transformer.resblocks." in name:
              _lowerCamelCase				:  Dict						=		name.replace('''text_encoder.transformer.resblocks.'''			,     '''text_model.encoder.layers.'''   )
       if "ln_1" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''ln_1'''			,     '''layer_norm1'''   )
       if "ln_2" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''ln_2'''			,     '''layer_norm2'''   )
       if "c_fc" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''c_fc'''			,     '''fc1'''   )
       if "c_proj" in name:
              _lowerCamelCase				:  str						=		name.replace('''c_proj'''			,     '''fc2'''   )
       if "text_encoder" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''text_encoder'''			,     '''text_model'''   )
       if "ln_final" in name:
              _lowerCamelCase				:  str						=		name.replace('''ln_final'''			,     '''final_layer_norm'''   )
       # projection layers
       if "img_projector.linear_hidden." in name:
              _lowerCamelCase				:  str						=		name.replace('''img_projector.linear_hidden.'''			,     '''visual_projection.'''   )
       if "img_projector.linear_out." in name:
              _lowerCamelCase				:  Union[str, Any]						=		name.replace('''img_projector.linear_out.'''			,     '''visual_projection.3.'''   )
       if "text_projector.linear_hidden" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''text_projector.linear_hidden'''			,     '''text_projection'''   )
       if "text_projector.linear_out" in name:
              _lowerCamelCase				:  Any						=		name.replace('''text_projector.linear_out'''			,     '''text_projection.3'''   )

       return name




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       for key in orig_state_dict.copy().keys():
              _lowerCamelCase				:  Optional[int]						=		orig_state_dict.pop(lowercase_   )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Tuple						=		key.split('''.'''   )
                     _lowerCamelCase,	_lowerCamelCase				:  Dict						=		int(key_split[2]   ), int(key_split[4]   )
                     _lowerCamelCase				:  Optional[int]						=		config.vision_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  List[Any]						=		val[:dim, :]
                            _lowerCamelCase				:  List[Any]						=		val[dim : dim * 2, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  Union[str, Any]						=		val[:dim]
                            _lowerCamelCase				:  Optional[Any]						=		val[dim : dim * 2]
                            _lowerCamelCase				:  Any						=		val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Optional[int]						=		key.split('''.'''   )
                     _lowerCamelCase				:  Optional[Any]						=		int(key_split[3]   )
                     _lowerCamelCase				:  Union[str, Any]						=		config.text_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  Any						=		val[:dim, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase				:  Dict						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  List[Any]						=		val[:dim]
                            _lowerCamelCase				:  Tuple						=		val[dim : dim * 2]
                            _lowerCamelCase				:  str						=		val[-dim:]
              else:
                     _lowerCamelCase				:  Optional[Any]						=		rename_key(lowercase_   )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            _lowerCamelCase				:  List[str]						=		val.squeeze_()
                     else:
                            _lowerCamelCase				:  Tuple						=		val

       return orig_state_dict




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Union[str, Any]						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase				:  Any						=		Image.open(requests.get(lowercase_			,     stream=lowercase_   ).raw   )
       return im




@torch.no_grad()
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_="groupvit-gcc-yfcc"			,     lowercase_=False   ):
       _lowerCamelCase				:  Optional[Any]						=		GroupViTConfig()
       _lowerCamelCase				:  Any						=		GroupViTModel(lowercase_   ).eval()

       _lowerCamelCase				:  Optional[Any]						=		torch.load(lowercase_			,     map_location='''cpu'''   )['''model''']
       _lowerCamelCase				:  List[str]						=		convert_state_dict(lowercase_			,     lowercase_   )
       _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		model.load_state_dict(lowercase_			,     strict=lowercase_   )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_   ) == 0)

       # verify result
       _lowerCamelCase				:  Optional[Any]						=		CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       _lowerCamelCase				:  Dict						=		prepare_img()
       _lowerCamelCase				:  str						=		processor(text=['''a photo of a cat''', '''a photo of a dog''']			,     images=lowercase_			,     padding=lowercase_			,     return_tensors='''pt'''   )

       with torch.no_grad():
              _lowerCamelCase				:  Optional[int]						=		model(**lowercase_   )

       if model_name == "groupvit-gcc-yfcc":
              _lowerCamelCase				:  Dict						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]   )
       elif model_name == "groupvit-gcc-redcaps":
              _lowerCamelCase				:  Tuple						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]   )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""   )
       assert torch.allclose(outputs.logits_per_image			,     lowercase_			,     atol=1e-3   )

       processor.save_pretrained(lowercase_   )
       model.save_pretrained(lowercase_   )
       print('''Successfully saved processor and model to'''			,     lowercase_   )

       if push_to_hub:
              print('''Pushing to the hub...'''   )
              processor.push_to_hub(lowercase_			,     organization='''nielsr'''   )
              model.push_to_hub(lowercase_			,     organization='''nielsr'''   )


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser()
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
       )
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
       parser.add_argument(
           '--model_name',
           default='groupvit-gccy-fcc',
           type=str,
           help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
       )
       _lowerCamelCase      							=   parser.parse_args()

       convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

