
"""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   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       tempfile.mkdtemp()

          # fmt: off
          UpperCAmelCase          =       ['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
          UpperCAmelCase          =       dict(zip(lowercase_ ,							range(len(lowercase_				)				)				)				)
          UpperCAmelCase          =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          UpperCAmelCase          =       {'unk_token': '<unk>'}

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

          UpperCAmelCase          =       {
              'do_resize': True,
              'size': 2_0,
              'do_center_crop': True,
              'crop_size': 1_8,
              'do_normalize': True,
              'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
          }
          UpperCAmelCase          =       os.path.join(self.tmpdirname ,							lowercase_				)
          with open(self.image_processor_file ,							'w' ,							encoding='utf-8'				) as fp:
              json.dump(lowercase_ ,							lowercase_				)




      def      a_   (				self ,							**lowercase_				)    ->       str:
          return CLIPTokenizer.from_pretrained(self.tmpdirname ,							**lowercase_				)




      def      a_   (				self ,							**lowercase_				)    ->       Dict:
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,							**lowercase_				)




      def      a_   (				self ,							**lowercase_				)    ->       Optional[Any]:
          return ViTImageProcessor.from_pretrained(self.tmpdirname ,							**lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          shutil.rmtree(self.tmpdirname				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       [np.random.randint(2_5_5 ,							size=(3, 3_0, 4_0_0) ,							dtype=np.uinta				)]

          UpperCAmelCase          =       [Image.fromarray(np.moveaxis(lowercase_ ,							0 ,							-1				)				) for x in image_inputs]

          return image_inputs




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.get_tokenizer()
          UpperCAmelCase          =       self.get_rust_tokenizer()
          UpperCAmelCase          =       self.get_image_processor()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)
          processor_slow.save_pretrained(self.tmpdirname				)
          UpperCAmelCase          =       CLIPSegProcessor.from_pretrained(self.tmpdirname ,							use_fast=lowercase_				)

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)
          processor_fast.save_pretrained(self.tmpdirname				)
          UpperCAmelCase          =       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 ,							lowercase_				)
          self.assertIsInstance(processor_fast.tokenizer ,							lowercase_				)

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




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=self.get_tokenizer() ,							image_processor=self.get_image_processor()				)
          processor.save_pretrained(self.tmpdirname				)

          UpperCAmelCase          =       self.get_tokenizer(bos_token='(BOS)' ,							eos_token='(EOS)'				)
          UpperCAmelCase          =       self.get_image_processor(do_normalize=lowercase_ ,							padding_value=1.0				)

          UpperCAmelCase          =       CLIPSegProcessor.from_pretrained(
              self.tmpdirname ,							bos_token='(BOS)' ,							eos_token='(EOS)' ,							do_normalize=lowercase_ ,							padding_value=1.0				)

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

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




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       image_processor(lowercase_ ,							return_tensors='np'				)
          UpperCAmelCase          =       processor(images=lowercase_ ,							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      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       'lower newer'

          UpperCAmelCase          =       processor(text=lowercase_				)

          UpperCAmelCase          =       tokenizer(lowercase_				)

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




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       'lower newer'
          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       processor(text=lowercase_ ,							images=lowercase_				)

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

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




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       self.prepare_image_inputs()
          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       processor(images=lowercase_ ,							visual_prompt=lowercase_				)

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

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




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       CLIPSegProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCAmelCase          =       processor.batch_decode(lowercase_				)
          UpperCAmelCase          =       tokenizer.batch_decode(lowercase_				)

          self.assertListEqual(lowercase_ ,							lowercase_				)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

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



