

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def       __snake_case						( __UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      if "img_encoder.pos_embed" in name:
            A_						  =				name.replace("img_encoder.pos_embed"				,"vision_model.embeddings.position_embeddings"    )
      if "img_encoder.patch_embed.proj" in name:
            A_						  =				name.replace("img_encoder.patch_embed.proj"				,"vision_model.embeddings.patch_embeddings.projection"    )
      if "img_encoder.patch_embed.norm" in name:
            A_						  =				name.replace("img_encoder.patch_embed.norm"				,"vision_model.embeddings.layernorm"    )
      if "img_encoder.layers" in name:
            A_						  =				name.replace("img_encoder.layers"				,"vision_model.encoder.stages"    )
      if "blocks" in name and "res" not in name:
            A_						  =				name.replace("blocks"				,"layers"    )
      if "attn" in name and "pre_assign" not in name:
            A_						  =				name.replace("attn"				,"self_attn"    )
      if "proj" in name and "self_attn" in name and "text" not in name:
            A_						  =				name.replace("proj"				,"out_proj"    )
      if "pre_assign_attn.attn.proj" in name:
            A_						  =				name.replace("pre_assign_attn.attn.proj"				,"pre_assign_attn.attn.out_proj"    )
      if "norm1" in name:
            A_						  =				name.replace("norm1"				,"layer_norm1"    )
      if "norm2" in name and "pre_assign" not in name:
            A_						  =				name.replace("norm2"				,"layer_norm2"    )
      if "img_encoder.norm" in name:
            A_						  =				name.replace("img_encoder.norm"				,"vision_model.layernorm"    )
      # text encoder
      if "text_encoder.token_embedding" in name:
            A_						  =				name.replace("text_encoder.token_embedding"				,"text_model.embeddings.token_embedding"    )
      if "text_encoder.positional_embedding" in name:
            A_						  =				name.replace("text_encoder.positional_embedding"				,"text_model.embeddings.position_embedding.weight"    )
      if "text_encoder.transformer.resblocks." in name:
            A_						  =				name.replace("text_encoder.transformer.resblocks."				,"text_model.encoder.layers."    )
      if "ln_1" in name:
            A_						  =				name.replace("ln_1"				,"layer_norm1"    )
      if "ln_2" in name:
            A_						  =				name.replace("ln_2"				,"layer_norm2"    )
      if "c_fc" in name:
            A_						  =				name.replace("c_fc"				,"fc1"    )
      if "c_proj" in name:
            A_						  =				name.replace("c_proj"				,"fc2"    )
      if "text_encoder" in name:
            A_						  =				name.replace("text_encoder"				,"text_model"    )
      if "ln_final" in name:
            A_						  =				name.replace("ln_final"				,"final_layer_norm"    )
      # projection layers
      if "img_projector.linear_hidden." in name:
            A_						  =				name.replace("img_projector.linear_hidden."				,"visual_projection."    )
      if "img_projector.linear_out." in name:
            A_						  =				name.replace("img_projector.linear_out."				,"visual_projection.3."    )
      if "text_projector.linear_hidden" in name:
            A_						  =				name.replace("text_projector.linear_hidden"				,"text_projection"    )
      if "text_projector.linear_out" in name:
            A_						  =				name.replace("text_projector.linear_out"				,"text_projection.3"    )

      return name

def       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            A_						  =				orig_state_dict.pop(__UpperCamelCase    )

            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
                  A_						  =				key.split("."    )
                  A_  ,     A_						  =				int(key_split[2]    ), int(key_split[4]    )
                  A_						  =				config.vision_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[dim : dim * 2, :]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				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
                  A_						  =				key.split("."    )
                  A_						  =				int(key_split[3]    )
                  A_						  =				config.text_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[
                            dim : dim * 2, :
                        ]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				val[-dim:]
            else:
                  A_						  =				rename_key(__UpperCamelCase    )
                  # 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
                  ):
                        A_						  =				val.squeeze_()
                  else:
                        A_						  =				val

      return orig_state_dict

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
      A_						  =				Image.open(requests.get(__UpperCamelCase				,stream=__UpperCamelCase    ).raw    )
      return im

@torch.no_grad()
def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]="groupvit-gcc-yfcc"				,__UpperCamelCase	: List[Any]=False    ):



      """simple docstring"""

      A_						  =				GroupViTConfig()
      A_						  =				GroupViTModel(__UpperCamelCase    ).eval()

      A_						  =				torch.load(__UpperCamelCase				,map_location="cpu"    )["model"]
      A_						  =				convert_state_dict(__UpperCamelCase				,__UpperCamelCase    )
      A_  ,     A_						  =				model.load_state_dict(__UpperCamelCase				,strict=__UpperCamelCase    )
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCamelCase    ) == 0)

      # verify result
      A_						  =				CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"    )
      A_						  =				prepare_img()
      A_						  =				processor(text=["a photo of a cat", "a photo of a dog"]				,images=__UpperCamelCase				,padding=__UpperCamelCase				,return_tensors="pt"    )

      with torch.no_grad():
            A_						  =				model(**__UpperCamelCase    )

      if model_name == "groupvit-gcc-yfcc":
            A_						  =				torch.tensor([[13.3523, 6.3629]]    )
      elif model_name == "groupvit-gcc-redcaps":
            A_						  =				torch.tensor([[16.1873, 8.6230]]    )
      else:
            raise ValueError(f'''Model name {model_name} not supported.'''    )
      assert torch.allclose(outputs.logits_per_image				,__UpperCamelCase				,atol=1E-3    )

      processor.save_pretrained(__UpperCamelCase    )
      model.save_pretrained(__UpperCamelCase    )
      print("Successfully saved processor and model to"				,__UpperCamelCase    )

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


if __name__ == "__main__":
      __a    :Tuple      =			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`.',
      )
      __a    :int      =			parser.parse_args()

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






"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
							__lowerCamelCase   :   Any       =output_stream[0]
							__lowerCamelCase   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

							__lowerCamelCase   :   Any       =[
							    '''ffmpeg''',
							    '''-f''',
							    format_,
							    '''-i''',
							    input_,
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-fflags''',
							    '''nobuffer''',
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]
							__lowerCamelCase   :   Tuple       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							__lowerCamelCase   :   int       =_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							for item in iterator:
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

							__lowerCamelCase   :   Union[str, Any]       =ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    format_for_conversion=SCREAMING_SNAKE_CASE )
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   int       =np.intaa
														__lowerCamelCase   :   Optional[int]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   Dict       =np.floataa
														__lowerCamelCase   :   Tuple       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

							__lowerCamelCase   :   List[str]       =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
							__lowerCamelCase   :   int       =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
							__lowerCamelCase   :   Optional[int]       =datetime.datetime.now()
							__lowerCamelCase   :   List[Any]       =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    stride=(stride_left, stride_right)     ,    stream=SCREAMING_SNAKE_CASE ):
														# Put everything back in numpy scale
														__lowerCamelCase   :   Optional[int]       =np.frombuffer(item['''raw''']     ,    dtype=SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Dict       =(
														    item['''stride'''][0] // size_of_sample,
														    item['''stride'''][1] // size_of_sample,
														)
														__lowerCamelCase   :   Optional[Any]       =sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =B''''''
							__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
							__lowerCamelCase   :   List[str]       =0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
																					__lowerCamelCase   :   List[str]       =(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
																												# We are flushing the accumulator
																												__lowerCamelCase   :   Optional[Any]       =(_stride_left, stride_right)
																												__lowerCamelCase   :   str       ={'''raw''': acc[:chunk_len], '''stride''': stride}
																												if stream:
																																			__lowerCamelCase   :   Optional[Any]       =False
																												yield item
																												__lowerCamelCase   :   Dict       =stride_left
																												__lowerCamelCase   :   str       =acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE ) > stride_left:
														__lowerCamelCase   :   List[str]       ={'''raw''': acc, '''stride''': (_stride_left, 0)}
														if stream:
																					__lowerCamelCase   :   Tuple       =False
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error




