


'''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 OwlViTImageProcessor, OwlViTProcessor
@require_vision
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Tuple:
						__A=	tempfile.mkdtemp()

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

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

						__A=	{
						    '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],
						}
						__A=	os.path.join(self.tmpdirname			,    lowerCAmelCase_	)
						with open(self.image_processor_file			,    'w'			,    encoding='utf-8'	) as fp:
									json.dump(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Dict			,    **lowerCAmelCase_							:			Dict	)      ->		Union[str, Any]:
						return CLIPTokenizer.from_pretrained(self.tmpdirname			,    pad_token='!'			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    **lowerCAmelCase_							:			Any	)      ->		Union[str, Any]:
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,    pad_token='!'			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    **lowerCAmelCase_							:			int	)      ->		Optional[Any]:
						return OwlViTImageProcessor.from_pretrained(self.tmpdirname			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						shutil.rmtree(self.tmpdirname	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						__A=	[np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta	)]

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

						return image_inputs
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.get_tokenizer()
						__A=	self.get_rust_tokenizer()
						__A=	self.get_image_processor()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)
						processor_slow.save_pretrained(self.tmpdirname	)
						__A=	OwlViTProcessor.from_pretrained(self.tmpdirname			,    use_fast=lowerCAmelCase_	)

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)
						processor_fast.save_pretrained(self.tmpdirname	)
						__A=	OwlViTProcessor.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			,    lowerCAmelCase_	)
						self.assertIsInstance(processor_fast.tokenizer			,    lowerCAmelCase_	)

						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			,    lowerCAmelCase_	)
						self.assertIsInstance(processor_fast.image_processor			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		str:
						__A=	OwlViTProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()	)
						processor.save_pretrained(self.tmpdirname	)

						__A=	self.get_tokenizer(bos_token='(BOS)'			,    eos_token='(EOS)'	)
						__A=	self.get_image_processor(do_normalize=lowerCAmelCase_	)

						__A=	OwlViTProcessor.from_pretrained(
						    self.tmpdirname			,    bos_token='(BOS)'			,    eos_token='(EOS)'			,    do_normalize=lowerCAmelCase_	)

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

						self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string()	)
						self.assertIsInstance(processor.image_processor			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)

						__A=	self.prepare_image_inputs()

						__A=	image_processor(lowerCAmelCase_			,    return_tensors='np'	)
						__A=	processor(images=lowerCAmelCase_			,    return_tensors='np'	)

						for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()			,    input_processor[key].sum()			,    delta=1E-2	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Any:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)

						__A=	'lower newer'

						__A=	processor(text=lowerCAmelCase_			,    return_tensors='np'	)

						__A=	tokenizer(lowerCAmelCase_			,    return_tensors='np'	)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()			,    encoded_processor[key][0].tolist()	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Union[str, Any]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)

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

						__A=	processor(text=lowerCAmelCase_			,    images=lowerCAmelCase_	)

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

						# test if it raises when no input is passed
						with pytest.raises(lowerCAmelCase_	):
									processor()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						__A=	'google/owlvit-base-patch32'
						__A=	OwlViTProcessor.from_pretrained(lowerCAmelCase_	)

						__A=	['cat', 'nasa badge']
						__A=	processor(text=lowerCAmelCase_	)

						__A=	16
						self.assertListEqual(list(inputs.keys()	)			,    ['input_ids', 'attention_mask']	)
						self.assertEqual(inputs['input_ids'].shape			,    (2, seq_length)	)

						# test if it raises when no input is passed
						with pytest.raises(lowerCAmelCase_	):
									processor()
			def 					lowerCAmelCase     (  self							:			Any	)      ->		str:
						__A=	'google/owlvit-base-patch32'
						__A=	OwlViTProcessor.from_pretrained(lowerCAmelCase_	)

						__A=	[['cat', 'nasa badge'], ['person']]
						__A=	processor(text=lowerCAmelCase_	)

						__A=	16
						__A=	len(lowerCAmelCase_	)
						__A=	max([len(lowerCAmelCase_	) for texts in input_texts]	)

						self.assertListEqual(list(inputs.keys()	)			,    ['input_ids', 'attention_mask']	)
						self.assertEqual(inputs['input_ids'].shape			,    (batch_size * num_max_text_queries, seq_length)	)

						# test if it raises when no input is passed
						with pytest.raises(lowerCAmelCase_	):
									processor()
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Dict:
						__A=	'google/owlvit-base-patch32'
						__A=	OwlViTProcessor.from_pretrained(lowerCAmelCase_	)

						__A=	['cat', 'nasa badge']
						__A=	processor(text=lowerCAmelCase_	)

						__A=	16
						__A=	inputs['input_ids']
						__A=	[
						    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						]

						self.assertListEqual(list(inputs.keys()	)			,    ['input_ids', 'attention_mask']	)
						self.assertEqual(inputs['input_ids'].shape			,    (2, seq_length)	)
						self.assertListEqual(list(input_ids[0]	)			,    predicted_ids[0]	)
						self.assertListEqual(list(input_ids[1]	)			,    predicted_ids[1]	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Tuple:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)

						__A=	self.prepare_image_inputs()
						__A=	self.prepare_image_inputs()

						__A=	processor(images=lowerCAmelCase_			,    query_images=lowerCAmelCase_	)

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

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

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	OwlViTProcessor(tokenizer=lowerCAmelCase_			,    image_processor=lowerCAmelCase_	)

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

						__A=	processor.batch_decode(lowerCAmelCase_	)
						__A=	tokenizer.batch_decode(lowerCAmelCase_	)

						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

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



