




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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      tempfile.mkdtemp()

							# fmt: off
							SCREAMING_SNAKE_CASE_   : Optional[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_   : Optional[int]					  =      dict(zip(snake_case__      ,range(len(snake_case__ ) ) ) )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
							SCREAMING_SNAKE_CASE_   : Dict					  =      {'unk_token': '<unk>'}

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file'] )
							SCREAMING_SNAKE_CASE_   : int					  =      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__ ) )

							SCREAMING_SNAKE_CASE_   : Optional[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_   : Tuple					  =      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 		snake_case	(			self      ,**snake_case__ ):
							return CLIPTokenizer.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,**snake_case__ ):
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,**snake_case__ ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self ):
							shutil.rmtree(self.tmpdirname )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [np.random.randint(255      ,size=(3, 30, 400)      ,dtype=np.uinta )]

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [Image.fromarray(np.moveaxis(snake_case__      ,0      ,-1 ) ) for x in image_inputs]

							return image_inputs






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.get_image_processor()

							SCREAMING_SNAKE_CASE_   : Any					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )
							processor_slow.save_pretrained(self.tmpdirname )
							SCREAMING_SNAKE_CASE_   : Dict					  =      CLIPSegProcessor.from_pretrained(self.tmpdirname      ,use_fast=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )
							processor_fast.save_pretrained(self.tmpdirname )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

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

							SCREAMING_SNAKE_CASE_   : Any					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Dict					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )

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

							SCREAMING_SNAKE_CASE_   : Any					  =      image_processor(snake_case__      ,return_tensors='np' )
							SCREAMING_SNAKE_CASE_   : str					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      'lower newer'

							SCREAMING_SNAKE_CASE_   : List[str]					  =      processor(text=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer(snake_case__ )

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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : str					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Tuple					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      'lower newer'
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_   : int					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Any					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : str					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      self.prepare_image_inputs()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_   : str					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      CLIPSegProcessor(tokenizer=snake_case__      ,image_processor=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor.batch_decode(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer.batch_decode(snake_case__ )

							self.assertListEqual(snake_case__      ,snake_case__ )











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

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




