






"""simple docstring"""



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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Dict		  =  tempfile.mkdtemp()

													# fmt: off
													A__						:  Union[str, 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__						:  List[Any]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Optional[int]		  =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
													A__						:  Optional[Any]		  =  {"""unk_token""": """<unk>"""}

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

													A__						:  str		  =  {
													    """do_resize""": True,
													    """size""": 20,
													    """do_center_crop""": True,
													    """crop_size""": 18,
													    """do_normalize""": True,
													    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
													    """image_std""": [0.26862954, 0.26130258, 0.27577711],
													}
													A__						:  int		  =  os.path.join(self.tmpdirname			,    snake_case							)
													with open(self.image_processor_file			,    """w"""			,    encoding="""utf-8"""							) as fp:
																			json.dump(snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    **snake_case      :						Tuple							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						List[Any]			,    **snake_case      :						Any							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Dict			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													shutil.rmtree(self.tmpdirname							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  [np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta							)]

													A__						:  str		  =  [Image.fromarray(np.moveaxis(snake_case			,    0			,    -1							)							) for x in image_inputs]

													return image_inputs


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





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

													A__						:  Any		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)
													processor_slow.save_pretrained(self.tmpdirname							)
													A__						:  int		  =  CLIPSegProcessor.from_pretrained(self.tmpdirname			,    use_fast=snake_case							)

													A__						:  List[Any]		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)
													processor_fast.save_pretrained(self.tmpdirname							)
													A__						:  Optional[Any]		  =  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			,    snake_case							)
													self.assertIsInstance(processor_fast.tokenizer			,    snake_case							)

													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			,    snake_case							)
													self.assertIsInstance(processor_fast.image_processor			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





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

													A__						:  Tuple		  =  self.get_tokenizer(bos_token="""(BOS)"""			,    eos_token="""(EOS)"""							)
													A__						:  str		  =  self.get_image_processor(do_normalize=snake_case			,    padding_value=1.0							)

													A__						:  str		  =  CLIPSegProcessor.from_pretrained(
													    self.tmpdirname			,    bos_token="""(BOS)"""			,    eos_token="""(EOS)"""			,    do_normalize=snake_case			,    padding_value=1.0							)

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

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


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.get_image_processor()
													A__						:  List[Any]		  =  self.get_tokenizer()

													A__						:  int		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)

													A__						:  Optional[Any]		  =  self.prepare_image_inputs()

													A__						:  Dict		  =  image_processor(snake_case			,    return_tensors="""np"""							)
													A__						:  Union[str, Any]		  =  processor(images=snake_case			,    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 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Any		  =  self.get_image_processor()
													A__						:  int		  =  self.get_tokenizer()

													A__						:  Optional[Any]		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)

													A__						:  List[Any]		  =  """lower newer"""

													A__						:  Optional[int]		  =  processor(text=snake_case							)

													A__						:  str		  =  tokenizer(snake_case							)

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


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  str		  =  self.get_image_processor()
													A__						:  int		  =  self.get_tokenizer()

													A__						:  Optional[int]		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)

													A__						:  Optional[int]		  =  """lower newer"""
													A__						:  Optional[int]		  =  self.prepare_image_inputs()

													A__						:  str		  =  processor(text=snake_case			,    images=snake_case							)

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

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


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  int		  =  self.get_image_processor()
													A__						:  int		  =  self.get_tokenizer()

													A__						:  Dict		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)

													A__						:  List[str]		  =  self.prepare_image_inputs()
													A__						:  str		  =  self.prepare_image_inputs()

													A__						:  List[Any]		  =  processor(images=snake_case			,    visual_prompt=snake_case							)

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

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


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





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

													A__						:  Dict		  =  CLIPSegProcessor(tokenizer=snake_case			,    image_processor=snake_case							)

													A__						:  Union[str, Any]		  =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A__						:  Dict		  =  processor.batch_decode(snake_case							)
													A__						:  List[Any]		  =  tokenizer.batch_decode(snake_case							)

													self.assertListEqual(snake_case			,    snake_case							)














"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    =							logging.get_logger(__name__)

A_    =							{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'roc_bert'


							def __init__(			self      :						Tuple			,    snake_case      :						Union[str, Any]=3_0522			,    snake_case      :						Optional[Any]=768			,    snake_case      :						Any=12			,    snake_case      :						Tuple=12			,    snake_case      :						Any=3072			,    snake_case      :						Tuple="gelu"			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						List[str]=512			,    snake_case      :						int=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						str=1e-12			,    snake_case      :						int=True			,    snake_case      :						Optional[Any]=0			,    snake_case      :						str="absolute"			,    snake_case      :						int=None			,    snake_case      :						int=True			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=768			,    snake_case      :						Dict=910			,    snake_case      :						Tuple=512			,    snake_case      :						Tuple=2_4858			,    snake_case      :						Optional[Any]=True			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  hidden_size
													A__						:  Optional[int]		  =  num_hidden_layers
													A__						:  Optional[Any]		  =  num_attention_heads
													A__						:  int		  =  intermediate_size
													A__						:  Optional[Any]		  =  hidden_act
													A__						:  Any		  =  hidden_dropout_prob
													A__						:  Tuple		  =  attention_probs_dropout_prob
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  str		  =  type_vocab_size
													A__						:  Any		  =  layer_norm_eps
													A__						:  List[str]		  =  use_cache
													A__						:  List[str]		  =  enable_pronunciation
													A__						:  int		  =  enable_shape
													A__						:  Tuple		  =  pronunciation_embed_dim
													A__						:  str		  =  pronunciation_vocab_size
													A__						:  Optional[Any]		  =  shape_embed_dim
													A__						:  str		  =  shape_vocab_size
													A__						:  List[str]		  =  concat_input
													A__						:  str		  =  position_embedding_type
													A__						:  str		  =  classifier_dropout
													super().__init__(pad_token_id=snake_case			,    **snake_case							)






