






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


a	: int      =				logging.get_logger(__name__)

a	: Any      =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

a	: Optional[int]      =				{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
}

a	: List[str]      =				{
    "Salesforce/codegen-350M-mono": 2_048,
}






@lru_cache()
def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[int]     =						(
      list(range(ord("""!"""	)						,	ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)						,	ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)						,	ord("""ÿ"""	) + 1	)	)
  )
  __UpperCAmelCase							:			Tuple     =						bs[:]
  __UpperCAmelCase							:			Any     =						0
  for b in range(2**8	):
    if b not in bs:
      bs.append(__lowerCamelCase	)
      cs.append(2**8 + n	)
      n += 1
  __UpperCAmelCase							:			Optional[Any]     =						[chr(__lowerCamelCase	) for n in cs]
  return dict(zip(__lowerCamelCase						,	__lowerCamelCase	)	)






def        lowerCamelCase__   (					__lowerCamelCase    :  List[Any]	):
  __UpperCAmelCase							:			Tuple     =						set()
  __UpperCAmelCase							:			List[Any]     =						word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    __UpperCAmelCase							:			Optional[int]     =						char
  return pairs







class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				int							 =			VOCAB_FILES_NAMES
      a						:				int							 =			PRETRAINED_VOCAB_FILES_MAP
      a						:				str							 =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						:				str							 =			['input_ids', 'attention_mask']
      def __init__(   self      :    List[Any]	,   __lowercase      :    Dict	,   __lowercase      :    Tuple	,   __lowercase      :    Dict="replace"	,   __lowercase      :    Optional[Any]="<|endoftext|>"	,   __lowercase      :    int="<|endoftext|>"	,   __lowercase      :    Any="<|endoftext|>"	,   __lowercase      :    str=None	,   __lowercase      :    Any=False	,   __lowercase      :    Dict=False	,   **__lowercase      :    str	,   )							->   List[str]:
        __UpperCAmelCase							:			Union[str, Any]     =						AddedToken(__lowercase	,   lstrip=__lowercase	,   rstrip=__lowercase      ) if isinstance(__lowercase	,   __lowercase      ) else bos_token
        __UpperCAmelCase							:			Dict     =						AddedToken(__lowercase	,   lstrip=__lowercase	,   rstrip=__lowercase      ) if isinstance(__lowercase	,   __lowercase      ) else eos_token
        __UpperCAmelCase							:			Optional[Any]     =						AddedToken(__lowercase	,   lstrip=__lowercase	,   rstrip=__lowercase      ) if isinstance(__lowercase	,   __lowercase      ) else unk_token
        __UpperCAmelCase							:			Optional[Any]     =						AddedToken(__lowercase	,   lstrip=__lowercase	,   rstrip=__lowercase      ) if isinstance(__lowercase	,   __lowercase      ) else pad_token
        super().__init__(
            errors=__lowercase	,   unk_token=__lowercase	,   bos_token=__lowercase	,   eos_token=__lowercase	,   pad_token=__lowercase	,   add_prefix_space=__lowercase	,   add_bos_token=__lowercase	,   **__lowercase	,   )
        __UpperCAmelCase							:			Any     =						add_bos_token

        with open(__lowercase	,   encoding="""utf-8"""      ) as vocab_handle:
          __UpperCAmelCase							:			int     =						json.load(__lowercase      )
        __UpperCAmelCase							:			str     =						{v: k for k, v in self.encoder.items()}
        __UpperCAmelCase							:			List[str]     =						errors  # how to handle errors in decoding
        __UpperCAmelCase							:			str     =						bytes_to_unicode()
        __UpperCAmelCase							:			str     =						{v: k for k, v in self.byte_encoder.items()}
        with open(__lowercase	,   encoding="""utf-8"""      ) as merges_handle:
          __UpperCAmelCase							:			str     =						merges_handle.read().split("""\n"""      )[1:-1]
        __UpperCAmelCase							:			List[Any]     =						[tuple(merge.split()      ) for merge in bpe_merges]
        __UpperCAmelCase							:			List[str]     =						dict(zip(__lowercase	,   range(len(__lowercase      )      )      )      )
        __UpperCAmelCase							:			List[str]     =						{}
        __UpperCAmelCase							:			Tuple     =						add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __UpperCAmelCase							:			Optional[int]     =						re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""      )
      @property
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   List[Any]:
        return len(self.encoder      )
      def 			UpperCAmelCase   (   self      :    Dict      )							->   Union[str, Any]:
        return dict(self.encoder	,   **self.added_tokens_encoder      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    List[str]      )							->   str:
        if token in self.cache:
          return self.cache[token]
        __UpperCAmelCase							:			Union[str, Any]     =						tuple(__lowercase      )
        __UpperCAmelCase							:			List[str]     =						get_pairs(__lowercase      )

        if not pairs:
          return token

        while True:
          __UpperCAmelCase							:			List[Any]     =						min(__lowercase	,   key=lambda __lowercase      : self.bpe_ranks.get(__lowercase	,   float("""inf"""      )      )      )
          if bigram not in self.bpe_ranks:
            break
          __UpperCAmelCase   ,				__UpperCAmelCase							:			List[str]     =						bigram
          __UpperCAmelCase							:			Optional[int]     =						[]
          __UpperCAmelCase							:			Tuple     =						0
          while i < len(__lowercase      ):
            try:
              __UpperCAmelCase							:			Tuple     =						word.index(__lowercase	,   __lowercase      )
            except ValueError:
              new_word.extend(word[i:]      )
              break
            else:
              new_word.extend(word[i:j]      )
              __UpperCAmelCase							:			Any     =						j

            if word[i] == first and i < len(__lowercase      ) - 1 and word[i + 1] == second:
              new_word.append(first + second      )
              i += 2
            else:
              new_word.append(word[i]      )
              i += 1
          __UpperCAmelCase							:			Any     =						tuple(__lowercase      )
          __UpperCAmelCase							:			Any     =						new_word
          if len(__lowercase      ) == 1:
            break
          else:
            __UpperCAmelCase							:			List[Any]     =						get_pairs(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						""" """.join(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						word
        return word
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    Any	,   __lowercase      :    Tuple=None      )							->   Dict:
        if self.add_bos_token:
          __UpperCAmelCase							:			List[str]     =						[self.bos_token_id]
        else:
          __UpperCAmelCase							:			List[str]     =						[]

        __UpperCAmelCase							:			Optional[int]     =						bos_token_ids + token_ids_a

        if token_ids_a is None:
          return output

        return output + bos_token_ids + token_ids_a
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    Optional[Any]      )							->   List[str]:
        __UpperCAmelCase							:			Dict     =						[]
        for token in re.findall(self.pat	,   __lowercase      ):
          __UpperCAmelCase							:			Tuple     =						"""""".join(
              self.byte_encoder[b] for b in token.encode("""utf-8"""      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase      ).split(""" """      )      )
        return bpe_tokens
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Tuple      )							->   Optional[Any]:
        return self.encoder.get(__lowercase	,   self.encoder.get(self.unk_token      )      )
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    int      )							->   Any:
        return self.decoder.get(__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    Optional[Any]      )							->   int:
        __UpperCAmelCase							:			Optional[Any]     =						"""""".join(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						bytearray([self.byte_decoder[c] for c in text]      ).decode("""utf-8"""	,   errors=self.errors      )
        return text
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    str	,   __lowercase      :    Optional[str] = None      )							->   Tuple[str]:
        if not os.path.isdir(__lowercase      ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
        __UpperCAmelCase							:			List[str]     =						os.path.join(
            __lowercase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
        __UpperCAmelCase							:			int     =						os.path.join(
            __lowercase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )

        with open(__lowercase	,   """w"""	,   encoding="""utf-8"""      ) as f:
          f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=__lowercase	,   ensure_ascii=__lowercase      ) + """\n"""      )

        __UpperCAmelCase							:			Dict     =						0
        with open(__lowercase	,   """w"""	,   encoding="""utf-8"""      ) as writer:
          writer.write("""#version: 0.2\n"""      )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda __lowercase      : kv[1]      ):
            if index != token_index:
              logger.warning(
                  f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  """ Please check that the tokenizer is not corrupted!"""      )
              __UpperCAmelCase							:			Optional[Any]     =						token_index
            writer.write(""" """.join(__lowercase      ) + """\n"""      )
            index += 1

        return vocab_file, merge_file
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    int	,   __lowercase      :    int=False	,   **__lowercase      :    Optional[int]      )							->   List[Any]:
        __UpperCAmelCase							:			Optional[int]     =						kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space      )
        if is_split_into_words or add_prefix_space:
          __UpperCAmelCase							:			Union[str, Any]     =						""" """ + text
        return (text, kwargs)
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,   __lowercase      :    bool = False	,   __lowercase      :    bool = None	,   __lowercase      :    Optional[List[str]] = None	,   **__lowercase      :    int	,   )							->   str:
        __UpperCAmelCase							:			List[Any]     =						super()._decode(
            token_ids=__lowercase	,   skip_special_tokens=__lowercase	,   clean_up_tokenization_spaces=__lowercase	,   **__lowercase	,   )

        if truncate_before_pattern is not None and len(__lowercase      ) > 0:
          __UpperCAmelCase							:			Optional[Any]     =						self.truncate(__lowercase	,   __lowercase      )

        return decoded_text




      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    Optional[int]      )							->   List[Any]:
        def find_re(__lowercase      :    str	,   __lowercase      :    Any	,   __lowercase      :    int      ):
          __UpperCAmelCase							:			Tuple     =						pattern.search(__lowercase	,   __lowercase      )
          return m.start() if m else -1

        __UpperCAmelCase							:			Union[str, Any]     =						[re.compile(__lowercase	,   re.MULTILINE      ) for pattern in truncate_before_pattern]

        __UpperCAmelCase							:			Tuple     =						list(re.finditer("""^print"""	,   __lowercase	,   re.MULTILINE      )      )

        if len(__lowercase      ) > 1:
          __UpperCAmelCase							:			int     =						completion[: prints[1].start()]

        __UpperCAmelCase							:			Union[str, Any]     =						list(re.finditer("""^def"""	,   __lowercase	,   re.MULTILINE      )      )

        if len(__lowercase      ) > 1:
          __UpperCAmelCase							:			Optional[int]     =						completion[: defs[1].start()]

        __UpperCAmelCase							:			Union[str, Any]     =						0

        __UpperCAmelCase							:			Optional[int]     =						[
            pos for pos in [find_re(__lowercase	,   __lowercase	,   __lowercase      ) for terminal in terminals] if pos != -1
        ]

        if len(__lowercase      ) > 0:
          return completion[: min(__lowercase      )]
        else:
          return completion










import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)






def        lowerCamelCase__   (					__lowerCamelCase    :  int	):
  __UpperCAmelCase							:			Any     =						CLIPSegTextConfig()
  __UpperCAmelCase							:			Optional[int]     =						CLIPSegVisionConfig(patch_size=16	)

  __UpperCAmelCase							:			List[str]     =						True if """refined""" in model_name else False
  __UpperCAmelCase							:			List[Any]     =						16 if """rd16""" in model_name else 64

  __UpperCAmelCase							:			Tuple     =						CLIPSegConfig.from_text_vision_configs(
      __lowerCamelCase						,	__lowerCamelCase						,	use_complex_transposed_convolution=__lowerCamelCase						,	reduce_dim=__lowerCamelCase						,	)
  return config






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  # update prefixes
  if "clip_model" in name:
    __UpperCAmelCase							:			int     =						name.replace("""clip_model"""						,	"""clip"""	)
  if "transformer" in name:
    if "visual" in name:
      __UpperCAmelCase							:			List[Any]     =						name.replace("""visual.transformer"""						,	"""vision_model"""	)
    else:
      __UpperCAmelCase							:			str     =						name.replace("""transformer"""						,	"""text_model"""	)
  if "resblocks" in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""resblocks"""						,	"""encoder.layers"""	)
  if "ln_1" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""ln_1"""						,	"""layer_norm1"""	)
  if "ln_2" in name:
    __UpperCAmelCase							:			str     =						name.replace("""ln_2"""						,	"""layer_norm2"""	)
  if "c_fc" in name:
    __UpperCAmelCase							:			Tuple     =						name.replace("""c_fc"""						,	"""fc1"""	)
  if "c_proj" in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""c_proj"""						,	"""fc2"""	)
  if "attn" in name and "self" not in name:
    __UpperCAmelCase							:			Tuple     =						name.replace("""attn"""						,	"""self_attn"""	)
  # text encoder
  if "token_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""token_embedding"""						,	"""text_model.embeddings.token_embedding"""	)
  if "positional_embedding" in name and "visual" not in name:
    __UpperCAmelCase							:			Dict     =						name.replace("""positional_embedding"""						,	"""text_model.embeddings.position_embedding.weight"""	)
  if "ln_final" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""ln_final"""						,	"""text_model.final_layer_norm"""	)
  # vision encoder
  if "visual.class_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.class_embedding"""						,	"""vision_model.embeddings.class_embedding"""	)
  if "visual.conv1" in name:
    __UpperCAmelCase							:			str     =						name.replace("""visual.conv1"""						,	"""vision_model.embeddings.patch_embedding"""	)
  if "visual.positional_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.positional_embedding"""						,	"""vision_model.embeddings.position_embedding.weight"""	)
  if "visual.ln_pre" in name:
    __UpperCAmelCase							:			List[Any]     =						name.replace("""visual.ln_pre"""						,	"""vision_model.pre_layrnorm"""	)
  if "visual.ln_post" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.ln_post"""						,	"""vision_model.post_layernorm"""	)
  # projection layers
  if "visual.proj" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.proj"""						,	"""visual_projection.weight"""	)
  if "text_projection" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""text_projection"""						,	"""text_projection.weight"""	)
  # decoder
  if "trans_conv" in name:
    __UpperCAmelCase							:			str     =						name.replace("""trans_conv"""						,	"""transposed_convolution"""	)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    __UpperCAmelCase							:			int     =						"""decoder.""" + name
  if "blocks" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""blocks"""						,	"""decoder.layers"""	)
  if "linear1" in name:
    __UpperCAmelCase							:			str     =						name.replace("""linear1"""						,	"""mlp.fc1"""	)
  if "linear2" in name:
    __UpperCAmelCase							:			str     =						name.replace("""linear2"""						,	"""mlp.fc2"""	)
  if "norm1" in name and "layer_" not in name:
    __UpperCAmelCase							:			int     =						name.replace("""norm1"""						,	"""layer_norm1"""	)
  if "norm2" in name and "layer_" not in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""norm2"""						,	"""layer_norm2"""	)

  return name






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  int	):
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase							:			Optional[Any]     =						orig_state_dict.pop(__lowerCamelCase	)

    if key.startswith("""clip_model"""	) and "attn.in_proj" in key:
      __UpperCAmelCase							:			Dict     =						key.split("""."""	)
      if "visual" in key:
        __UpperCAmelCase							:			List[str]     =						int(key_split[4]	)
        __UpperCAmelCase							:			Union[str, Any]     =						config.vision_config.hidden_size
        __UpperCAmelCase							:			List[Any]     =						"""vision_model"""
      else:
        __UpperCAmelCase							:			Optional[Any]     =						int(key_split[3]	)
        __UpperCAmelCase							:			str     =						config.text_config.hidden_size
        __UpperCAmelCase							:			Optional[Any]     =						"""text_model"""

      if "weight" in key:
        __UpperCAmelCase							:			Optional[Any]     =						val[:dim, :]
        __UpperCAmelCase							:			Any     =						val[
            dim : dim * 2, :
        ]
        __UpperCAmelCase							:			List[str]     =						val[-dim:, :]
      else:
        __UpperCAmelCase							:			List[str]     =						val[:dim]
        __UpperCAmelCase							:			Optional[int]     =						val[dim : dim * 2]
        __UpperCAmelCase							:			List[str]     =						val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      __UpperCAmelCase							:			Union[str, Any]     =						key.split("""."""	)
      __UpperCAmelCase							:			List[str]     =						int(key_split[1]	)
      __UpperCAmelCase							:			List[str]     =						config.reduce_dim
      if "weight" in key:
        __UpperCAmelCase							:			Optional[int]     =						val[:dim, :]
        __UpperCAmelCase							:			List[str]     =						val[dim : dim * 2, :]
        __UpperCAmelCase							:			Union[str, Any]     =						val[-dim:, :]
      else:
        __UpperCAmelCase							:			Tuple     =						val[:dim]
        __UpperCAmelCase							:			Any     =						val[dim : dim * 2]
        __UpperCAmelCase							:			int     =						val[-dim:]
    else:
      __UpperCAmelCase							:			List[Any]     =						rename_key(__lowerCamelCase	)
      if "visual_projection" in new_name or "text_projection" in new_name:
        __UpperCAmelCase							:			List[Any]     =						val.T
      __UpperCAmelCase							:			Any     =						val

  return orig_state_dict






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:			List[Any]     =						Image.open(requests.get(__lowerCamelCase						,	stream=__lowerCamelCase	).raw	)
  return image






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]						,	__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			Optional[Any]     =						get_clipseg_config(__lowerCamelCase	)
  __UpperCAmelCase							:			Tuple     =						CLIPSegForImageSegmentation(__lowerCamelCase	)
  model.eval()

  __UpperCAmelCase							:			Optional[int]     =						torch.load(__lowerCamelCase						,	map_location="""cpu"""	)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith("""model"""	):
      state_dict.pop(__lowerCamelCase						,	__lowerCamelCase	)

    # rename some keys
  __UpperCAmelCase							:			Optional[Any]     =						convert_state_dict(__lowerCamelCase						,	__lowerCamelCase	)
  __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[Any]     =						model.load_state_dict(__lowerCamelCase						,	strict=__lowerCamelCase	)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError("""Missing keys that are not expected: {}""".format(__lowerCamelCase	)	)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(f"""Unexpected keys: {unexpected_keys}"""	)

  __UpperCAmelCase							:			Optional[Any]     =						ViTImageProcessor(size=352	)
  __UpperCAmelCase							:			Tuple     =						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
  __UpperCAmelCase							:			List[Any]     =						CLIPSegProcessor(image_processor=__lowerCamelCase						,	tokenizer=__lowerCamelCase	)

  __UpperCAmelCase							:			int     =						prepare_img()
  __UpperCAmelCase							:			Optional[Any]     =						["""a glass""", """something to fill""", """wood""", """a jar"""]

  __UpperCAmelCase							:			Union[str, Any]     =						processor(text=__lowerCamelCase						,	images=[image] * len(__lowerCamelCase	)						,	padding="""max_length"""						,	return_tensors="""pt"""	)

  with torch.no_grad():
    __UpperCAmelCase							:			int     =						model(**__lowerCamelCase	)

  # verify values
  __UpperCAmelCase							:			Dict     =						torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
  __UpperCAmelCase							:			Union[str, Any]     =						torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
  if model_name == "clipseg-rd64-refined":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]	)
  elif model_name == "clipseg-rd64":
    __UpperCAmelCase							:			List[Any]     =						torch.tensor(
        [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]	)
  elif model_name == "clipseg-rd16":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]	)
  else:
    raise ValueError(f"""Model name {model_name} not supported."""	)

  assert torch.allclose(outputs.logits[0, :3, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  assert torch.allclose(outputs.pooled_output[0, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  print("""Looks ok!"""	)

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and processor to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(__lowerCamelCase	)
    processor.save_pretrained(__lowerCamelCase	)

  if push_to_hub:
    print(f"""Pushing model and processor for {model_name} to the hub"""	)
    model.push_to_hub(f"""CIDAS/{model_name}"""	)
    processor.push_to_hub(f"""CIDAS/{model_name}"""	)


if __name__ == "__main__":
       a	: Optional[Any]      =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="clipseg-rd64",
           type=str,
           choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
           help=(
               "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
               " reduce dimension)"
           ),
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
           type=str,
           help=(
               "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
               " the decoder weights."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, 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."
       )

       a	: int      =				parser.parse_args()
       convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


