






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 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   tempfile.mkdtemp()

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

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

						SCREAMING_SNAKE_CASE_   :	Dict													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    '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],
						}
						SCREAMING_SNAKE_CASE_   :	List[str]													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return CLIPTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return ViTImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


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


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

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]

						return image_inputs


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

						SCREAMING_SNAKE_CASE_   :	str													=   CLIPSegProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   CLIPSegProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   CLIPSegProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	List[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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   CLIPSegProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

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

						SCREAMING_SNAKE_CASE_   :	int													=   CLIPSegProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

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

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


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

						SCREAMING_SNAKE_CASE_   :	int													=   CLIPSegProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   processor(images=UpperCamelCase__ ,    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_   :	Optional[int]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   CLIPSegProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   tokenizer(UpperCamelCase__       )

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


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

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

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

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

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

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   processor(images=UpperCamelCase__ ,    visual_prompt=UpperCamelCase__       )

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

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






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   CLIPSegProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

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

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

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""Translation""" ,	init=__A ,	repr=__A						)


		def __call__(						self       ):
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






		def   __snake_case			(						self       ):
						from .features import Value

						return {k: Value('string'       ) for k in sorted(self.languages       )}






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[List]				    =						None
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""TranslationVariableLanguages""" ,	init=__A ,	repr=__A						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(self.languages       ) if self.languages else None


		def __call__(						self       ):
						return pa.struct({'language': pa.list_(pa.string()       ), 'translation': pa.list_(pa.string()       )}       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for lang, text in translation_dict.items():
										if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														translation_tuples.append((lang, text)       )
										else:
														translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

						return {"language": languages, "translation": translations}






		def   __snake_case			(						self       ):
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'       )       ),
						    "translation": Sequence(Value('string'       )       ),
						}