






'''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      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tempfile.mkdtemp()

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

							SCREAMING_SNAKE_CASE__     :				Dict									=os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['''vocab_file''']      )
							SCREAMING_SNAKE_CASE__     :				List[str]									=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(_a      ) + '''\n'''      )
							with open(self.merges_file					,      '''w'''					,      encoding='''utf-8'''      ) as fp:
													fp.write('''\n'''.join(_a      )      )

							SCREAMING_SNAKE_CASE__     :				int									={
							    """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],
							}
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.join(self.tmpdirname					,      _a      )
							with open(self.image_processor_file					,      '''w'''					,      encoding='''utf-8'''      ) as fp:
													json.dump(_a					,      _a      )




	def      __magic_name__	(      self	:							Dict					,      **__lowercase	:							List[Any]      )	->						Any:
							return CLIPTokenizer.from_pretrained(self.tmpdirname					,      **_a      )




	def      __magic_name__	(      self	:							Union[str, Any]					,      **__lowercase	:							int      )	->						List[Any]:
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,      **_a      )




	def      __magic_name__	(      self	:							Optional[Any]					,      **__lowercase	:							Optional[Any]      )	->						List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname					,      **_a      )




	def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
							shutil.rmtree(self.tmpdirname      )




	def      __magic_name__	(      self	:							str      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				str									=[np.random.randint(2_55					,      size=(3, 30, 4_00)					,      dtype=np.uinta      )]

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[Image.fromarray(np.moveaxis(_a					,      0					,      -1      )      ) for x in image_inputs]

							return image_inputs




	def      __magic_name__	(      self	:							Optional[Any]      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_tokenizer()
							SCREAMING_SNAKE_CASE__     :				Dict									=self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.get_image_processor()

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

							SCREAMING_SNAKE_CASE__     :				Any									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )
							processor_fast.save_pretrained(self.tmpdirname      )
							SCREAMING_SNAKE_CASE__     :				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					,      _a      )
							self.assertIsInstance(processor_fast.tokenizer					,      _a      )

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




	def      __magic_name__	(      self	:							Any      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=CLIPSegProcessor(tokenizer=self.get_tokenizer()					,      image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_tokenizer(bos_token='''(BOS)'''					,      eos_token='''(EOS)'''      )
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_image_processor(do_normalize=_a					,      padding_value=1.0      )

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=CLIPSegProcessor.from_pretrained(
							    self.tmpdirname					,      bos_token='''(BOS)'''					,      eos_token='''(EOS)'''					,      do_normalize=_a					,      padding_value=1.0      )

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

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




	def      __magic_name__	(      self	:							Any      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_image_processor()
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.get_tokenizer()

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )

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

							SCREAMING_SNAKE_CASE__     :				Tuple									=image_processor(_a					,      return_tensors='''np'''      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=processor(images=_a					,      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      __magic_name__	(      self	:							Any      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				Dict									=self.get_image_processor()
							SCREAMING_SNAKE_CASE__     :				List[Any]									=self.get_tokenizer()

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )

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

							SCREAMING_SNAKE_CASE__     :				List[str]									=processor(text=_a      )

							SCREAMING_SNAKE_CASE__     :				int									=tokenizer(_a      )

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




	def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				str									=self.get_image_processor()
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_tokenizer()

							SCREAMING_SNAKE_CASE__     :				Any									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )

							SCREAMING_SNAKE_CASE__     :				Any									="""lower newer"""
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE__     :				int									=processor(text=_a					,      images=_a      )

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

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




	def      __magic_name__	(      self	:							Optional[Any]      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				str									=self.get_image_processor()
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_tokenizer()

							SCREAMING_SNAKE_CASE__     :				List[Any]									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )

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

							SCREAMING_SNAKE_CASE__     :				List[str]									=processor(images=_a					,      visual_prompt=_a      )

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

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




	def      __magic_name__	(      self	:							List[Any]      )	->						int:
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_image_processor()
							SCREAMING_SNAKE_CASE__     :				str									=self.get_tokenizer()

							SCREAMING_SNAKE_CASE__     :				int									=CLIPSegProcessor(tokenizer=_a					,      image_processor=_a      )

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

							SCREAMING_SNAKE_CASE__     :				List[Any]									=processor.batch_decode(_a      )
							SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer.batch_decode(_a      )

							self.assertListEqual(_a					,      _a      )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)