


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

 return name





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[str]		):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase_      =  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
   lowerCamelCase_      =  key.split("."		)
   lowerCamelCase_ ,lowerCamelCase_      =  int(key_split[2]		), int(key_split[4]		)
   lowerCamelCase_      =  config.vision_config.hidden_size
   if "weight" in key:
    lowerCamelCase_      =  val[:dim, :]
    lowerCamelCase_      =  val[dim : dim * 2, :]
    lowerCamelCase_      =  val[-dim:, :]
   else:
    lowerCamelCase_      =  val[:dim]
    lowerCamelCase_      =  val[dim : dim * 2]
    lowerCamelCase_      =  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_      =  key.split("."		)
   lowerCamelCase_      =  int(key_split[3]		)
   lowerCamelCase_      =  config.text_config.hidden_size
   if "weight" in key:
    lowerCamelCase_      =  val[:dim, :]
    lowerCamelCase_      =  val[
        dim : dim * 2, :
    ]
    lowerCamelCase_      =  val[-dim:, :]
   else:
    lowerCamelCase_      =  val[:dim]
    lowerCamelCase_      =  val[dim : dim * 2]
    lowerCamelCase_      =  val[-dim:]
  else:
   lowerCamelCase_      =  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
   ):
    lowerCamelCase_      =  val.squeeze_()
   else:
    lowerCamelCase_      =  val

 return orig_state_dict





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(__UpperCAmelCase					,				stream=__UpperCAmelCase		).raw		)
 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Any="groupvit-gcc-yfcc"					,				UpperCAmelCase_						:						Dict=False		):
 lowerCamelCase_      =  GroupViTConfig()
 lowerCamelCase_      =  GroupViTModel(__UpperCAmelCase		).eval()

 lowerCamelCase_      =  torch.load(__UpperCAmelCase					,				map_location="cpu"		)["model"]
 lowerCamelCase_      =  convert_state_dict(__UpperCAmelCase					,				__UpperCAmelCase		)
 lowerCamelCase_ ,lowerCamelCase_      =  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
 lowerCamelCase_      =  CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
 lowerCamelCase_      =  prepare_img()
 lowerCamelCase_      =  processor(text=["a photo of a cat", "a photo of a dog"]					,				images=__UpperCAmelCase					,				padding=__UpperCAmelCase					,				return_tensors="pt"		)

 with torch.no_grad():
  lowerCamelCase_      =  model(**__UpperCAmelCase		)

 if model_name == "groupvit-gcc-yfcc":
  lowerCamelCase_      =  torch.tensor([[13.3523, 6.3629]]		)
 elif model_name == "groupvit-gcc-redcaps":
  lowerCamelCase_      =  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_  :				int						= 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_  :				Dict						= parser.parse_args()

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









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

import numpy as np

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	bytes			,			__UpperCAmelCase						:	int							)					-> np.array:
      SCREAMING_SNAKE_CASE_							=			f"{sampling_rate}"
      SCREAMING_SNAKE_CASE_							=			'1'
      SCREAMING_SNAKE_CASE_							=			'f32le'
      SCREAMING_SNAKE_CASE_							=			[
          'ffmpeg',
          '-i',
          'pipe:0',
          '-ac',
          ac,
          '-ar',
          ar,
          '-f',
          format_for_conversion,
          '-hide_banner',
          '-loglevel',
          'quiet',
          'pipe:1',
      ]

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> int:
      SCREAMING_SNAKE_CASE_							=			f"{sampling_rate}"
      SCREAMING_SNAKE_CASE_							=			'1'
      if format_for_conversion == "s16le":
            SCREAMING_SNAKE_CASE_							=			2
      elif format_for_conversion == "f32le":
            SCREAMING_SNAKE_CASE_							=			4
      else:
            raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"							)

      SCREAMING_SNAKE_CASE_							=			platform.system()
      if system == "Linux":
            SCREAMING_SNAKE_CASE_							=			'alsa'
            SCREAMING_SNAKE_CASE_							=			'default'
      elif system == "Darwin":
            SCREAMING_SNAKE_CASE_							=			'avfoundation'
            SCREAMING_SNAKE_CASE_							=			':0'
      elif system == "Windows":
            SCREAMING_SNAKE_CASE_							=			'dshow'
            SCREAMING_SNAKE_CASE_							=			'default'

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	Optional[int] = None			,			__UpperCAmelCase						:	Optional[Union[Tuple[float, float], float]] = None			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> Tuple:
      if stream_chunk_s is not None:
            SCREAMING_SNAKE_CASE_							=			stream_chunk_s
      else:
            SCREAMING_SNAKE_CASE_							=			chunk_length_s

      SCREAMING_SNAKE_CASE_							=			ffmpeg_microphone(__UpperCAmelCase			,			__UpperCAmelCase			,			format_for_conversion=__UpperCAmelCase							)
      if format_for_conversion == "s16le":
            SCREAMING_SNAKE_CASE_							=			np.intaa
            SCREAMING_SNAKE_CASE_							=			2
      elif format_for_conversion == "f32le":
            SCREAMING_SNAKE_CASE_							=			np.floataa
            SCREAMING_SNAKE_CASE_							=			4
      else:
            raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"							)

      if stride_length_s is None:
            SCREAMING_SNAKE_CASE_							=			chunk_length_s / 6
      SCREAMING_SNAKE_CASE_							=			int(round(sampling_rate * chunk_length_s							)							) * size_of_sample
      if isinstance(__UpperCAmelCase			,			(int, float)							):
            SCREAMING_SNAKE_CASE_							=			[stride_length_s, stride_length_s]

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Tuple[int, int]			,			__UpperCAmelCase						:	bool = False							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			b''
      SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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}"							)
      SCREAMING_SNAKE_CASE_							=			0
      for raw in iterator:
            acc += raw
            if stream and len(__UpperCAmelCase							) < chunk_len:
                  SCREAMING_SNAKE_CASE_							=			(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(__UpperCAmelCase							) >= chunk_len:
                        # We are flushing the accumulator
                        SCREAMING_SNAKE_CASE_							=			(_stride_left, stride_right)
                        SCREAMING_SNAKE_CASE_							=			{'raw': acc[:chunk_len], 'stride': stride}
                        if stream:
                              SCREAMING_SNAKE_CASE_							=			False
                        yield item
                        SCREAMING_SNAKE_CASE_							=			stride_left
                        SCREAMING_SNAKE_CASE_							=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(__UpperCAmelCase							) > stride_left:
            SCREAMING_SNAKE_CASE_							=			{'raw': acc, 'stride': (_stride_left, 0)}
            if stream:
                  SCREAMING_SNAKE_CASE_							=			False
            yield item





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	int							)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			2**24  # 16Mo
      try:
            with subprocess.Popen(__UpperCAmelCase			,			stdout=subprocess.PIPE			,			bufsize=__UpperCAmelCase							) as ffmpeg_process:
                  while True:
                        SCREAMING_SNAKE_CASE_							=			ffmpeg_process.stdout.read(__UpperCAmelCase							)
                        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