





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase	:	Optional[int]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[Any]		     =       {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCAmelCase	:	str		     =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



  a__	:					Dict										={}
  with open(SCREAMING_SNAKE_CASE       ,       "r"						) as file:
    for line_number, line in enumerate(SCREAMING_SNAKE_CASE						):
      a__	:					Union[str, Any]										=line.strip()
      if line:
        a__	:					Union[str, Any]										=line.split()
        a__	:					Any										=line_number
        a__	:					Any										=words[0]
        a__	:					Optional[int]										=value
  return result
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   List[Any]						):





  """simple docstring"""



  for attribute in key.split("."						):
    a__	:					Union[str, Any]										=getattr(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  a__	:					List[str]										=None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(SCREAMING_SNAKE_CASE						):
      a__	:					Tuple										=PARAM_MAPPING[full_name.split("."						)[-1]]
      a__	:					Any										="param"

  if weight_type is not None and weight_type != "param":
    a__	:					int										=getattr(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						).shape
  elif weight_type is not None and weight_type == "param":
    a__	:					int										=hf_pointer
    for attribute in hf_param_name.split("."						):
      a__	:					Union[str, Any]										=getattr(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    a__	:					Union[str, Any]										=shape_pointer.shape

    # let's reduce dimension
    a__	:					Optional[int]										=value[0]
  else:
    a__	:					Union[str, Any]										=hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
        f''' {value.shape} for {full_name}'''						)

  if weight_type == "weight":
    a__	:					List[str]										=value
  elif weight_type == "weight_g":
    a__	:					List[Any]										=value
  elif weight_type == "weight_v":
    a__	:					Any										=value
  elif weight_type == "bias":
    a__	:					Optional[int]										=value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."						):
      a__	:					str										=getattr(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    a__	:					str										=value
  else:
    a__	:					str										=value

  logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   List[Any]						):





  """simple docstring"""



  a__	:					List[str]										=None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(SCREAMING_SNAKE_CASE						):
      a__	:					List[Any]										=PARAM_MAPPING[full_name.split("."						)[-1]]
      a__	:					List[Any]										="param"

  if weight_type is not None and weight_type != "param":
    a__	:					Union[str, Any]										=".".join([key, weight_type]						)
  elif weight_type is not None and weight_type == "param":
    a__	:					Union[str, Any]										=".".join([key, hf_param_name]						)
  else:
    a__	:					Dict										=key

  a__	:					Dict										=value if "lm_head" in full_key else value[0]


UpperCAmelCase	:	int		     =       {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=False
  for key, mapped_key in MAPPING.items():
    a__	:					Union[str, Any]										="wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
      a__	:					List[str]										=True
      if "*" in mapped_key:
        a__	:					Any										=name.split(SCREAMING_SNAKE_CASE						)[0].split("."						)[-2]
        a__	:					Tuple										=mapped_key.replace("*"       ,       SCREAMING_SNAKE_CASE						)
      if "weight_g" in name:
        a__	:					int										="weight_g"
      elif "weight_v" in name:
        a__	:					int										="weight_v"
      elif "bias" in name:
        a__	:					List[Any]										="bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        a__	:					int										="weight"
      else:
        a__	:					Tuple										=None
      if hf_dict is not None:
        rename_dict(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
      else:
        set_recursively(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
      return is_used
  return is_used
def      _A       (		SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Optional[int]						):





  """simple docstring"""



  a__	:					List[Any]										=[]
  a__	:					Tuple										=fairseq_model.state_dict()

  a__	:					Any										=hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    a__	:					List[str]										=False
    if "conv_layers" in name:
      load_conv_layer(
          SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       hf_model.config.feat_extract_norm == "group"       ,       )
      a__	:					List[str]										=True
    else:
      a__	:					Optional[Any]										=load_wavaveca_layer(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE						)

  logger.warning(f'''Unused weights: {unused_weights}'''						)
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



  a__	:					Tuple										=full_name.split("conv_layers."						)[-1]
  a__	:					Tuple										=name.split("."						)
  a__	:					str										=int(items[0]						)
  a__	:					Tuple										=int(items[1]						)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
      a__	:					str										=value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
      a__	:					List[Any]										=value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
      a__	:					int										=value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
      a__	:					Any										=value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
  else:
    unused_weights.append(SCREAMING_SNAKE_CASE						)
@torch.no_grad()
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       SCREAMING_SNAKE_CASE     :   List[Any]=True       ,       SCREAMING_SNAKE_CASE     :   int=False						):





  """simple docstring"""



  if config_path is not None:
    a__	:					Union[str, Any]										=WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					str										=WavaVecaConfig()

  if is_seq_class:
    a__	:					Optional[int]										=read_txt_into_dict(SCREAMING_SNAKE_CASE						)
    a__	:					List[str]										=idalabel
    a__	:					List[Any]										=WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE						)
    a__	:					Optional[Any]										=WavaVecaFeatureExtractor(
        feature_size=1       ,       sampling_rate=16_000       ,       padding_value=0       ,       do_normalize=SCREAMING_SNAKE_CASE       ,       return_attention_mask=SCREAMING_SNAKE_CASE       ,       )
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)

  elif is_finetuned:
    if dict_path:
      a__	:					Tuple										=Dictionary.load(SCREAMING_SNAKE_CASE						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a__	:					str										=target_dict.pad_index
      a__	:					Dict										=target_dict.bos_index
      a__	:					int										=target_dict.eos_index
      a__	:					str										=len(target_dict.symbols						)
      a__	:					Optional[Any]										=os.path.join(SCREAMING_SNAKE_CASE       ,       "vocab.json"						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(SCREAMING_SNAKE_CASE						)						)
        return
      os.makedirs(SCREAMING_SNAKE_CASE       ,       exist_ok=SCREAMING_SNAKE_CASE						)
      a__	:					Dict										=target_dict.indices

      # fairseq has the <pad> and <s> switched
      a__	:					Optional[Any]										=0
      a__	:					Any										=1
      with open(SCREAMING_SNAKE_CASE       ,       "w"       ,       encoding="utf-8"						) as vocab_handle:
        json.dump(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
      a__	:					Optional[int]										=WavaVecaCTCTokenizer(
          SCREAMING_SNAKE_CASE       ,       unk_token=target_dict.unk_word       ,       pad_token=target_dict.pad_word       ,       bos_token=target_dict.bos_word       ,       eos_token=target_dict.eos_word       ,       word_delimiter_token="|"       ,       do_lower_case=SCREAMING_SNAKE_CASE       ,       )
      a__	:					List[Any]										=True if config.feat_extract_norm == "layer" else False
      a__	:					Optional[int]										=WavaVecaFeatureExtractor(
          feature_size=1       ,       sampling_rate=16_000       ,       padding_value=0       ,       do_normalize=SCREAMING_SNAKE_CASE       ,       return_attention_mask=SCREAMING_SNAKE_CASE       ,       )
      a__	:					int										=WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE       ,       tokenizer=SCREAMING_SNAKE_CASE						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE						)

    a__	:					Optional[int]										=WavaVecaForCTC(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					int										=WavaVecaForPreTraining(SCREAMING_SNAKE_CASE						)

  if is_finetuned or is_seq_class:
    a__					,							a__					,							a__	:					Dict										=fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]       ,       arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
  else:
    a__	:					str										=argparse.Namespace(task="audio_pretraining"						)
    a__	:					Tuple										=fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE						)

    a__					,							a__					,							a__	:					Dict										=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,       task=SCREAMING_SNAKE_CASE						)

  a__	:					Optional[int]										=model[0].eval()

  recursively_load_weights(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       not is_finetuned						)

  hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     UpperCAmelCase	:	Any		     =       argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     parser.add_argument(
         """--is_seq_class""",
         action="""store_true""",
         help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
     )
     UpperCAmelCase	:	Union[str, Any]		     =       parser.parse_args()

     UpperCAmelCase	:	Optional[Any]		     =       not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )













import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

        # fmt: off
        a__	:					List[Any]										=["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        a__	:					str										=dict(zip(lowerCAmelCase__		,      range(len(lowerCAmelCase__							)							)							)							)
        a__	:					List[Any]										=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        a__	:					Optional[int]										={"unk_token": "<unk>"}

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]							)
        with open(self.vocab_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write(json.dumps(lowerCAmelCase__							) + "\n"							)
        with open(self.merges_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write("\n".join(lowerCAmelCase__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "do_normalize": True,
            "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					Dict										=CLIPSegProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()							)
        self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					str										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					int										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=processor(images=lowerCAmelCase__		,      return_tensors="np"							)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1E-2							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Any										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["input_ids", "attention_mask", "pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					int										=self.prepare_image_inputs()
        a__	:					Union[str, Any]										=self.prepare_image_inputs()

        a__	:					Tuple										=processor(images=lowerCAmelCase__		,      visual_prompt=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "conditional_pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






