
'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import OwlViTImageProcessor


class     _lowerCAmelCase					(					unittest.TestCase   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase=7						,						UpperCAmelCase=3						,						UpperCAmelCase=18						,						UpperCAmelCase=30						,						UpperCAmelCase=400						,						UpperCAmelCase=True						,						UpperCAmelCase=None						,						UpperCAmelCase=True						,						UpperCAmelCase=None						,						UpperCAmelCase=True						,						UpperCAmelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]						,						UpperCAmelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,						UpperCAmelCase=True						,						)  ->     Dict:
														_snake_case     =						parent
														_snake_case     =						batch_size
														_snake_case     =						num_channels
														_snake_case     =						image_size
														_snake_case     =						min_resolution
														_snake_case     =						max_resolution
														_snake_case     =						do_resize
														_snake_case     =						size if size is not None else {"""height""": 18, """width""": 18}
														_snake_case     =						do_center_crop
														_snake_case     =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
														_snake_case     =						do_normalize
														_snake_case     =						image_mean
														_snake_case     =						image_std
														_snake_case     =						do_convert_rgb





							def  lowercase							(self						)  ->     Optional[Any]:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_convert_rgb": self.do_convert_rgb,
														}


@require_torch
@require_vision
class     _lowerCAmelCase					(					__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		OwlViTImageProcessor if is_vision_available() else None

							def  lowercase							(self						)  ->     List[Any]:
														_snake_case     =						OwlViTImageProcessingTester(self						)

							@property
							def  lowercase							(self						)  ->     Any:
														return self.image_processor_tester.prepare_image_processor_dict()

							def  lowercase							(self						)  ->     Optional[int]:
														_snake_case     =						self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""do_resize"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""size"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""do_center_crop"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""center_crop"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""do_normalize"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""image_mean"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""image_std"""						)						)
														self.assertTrue(hasattr(UpperCAmelCase						,						"""do_convert_rgb"""						)						)

							def  lowercase							(self						)  ->     Optional[int]:
														_snake_case     =						self.image_processing_class.from_dict(self.image_processor_dict						)
														self.assertEqual(image_processor.size						,						{"""height""": 18, """width""": 18}						)
														self.assertEqual(image_processor.crop_size						,						{"""height""": 18, """width""": 18}						)

														_snake_case     =						self.image_processing_class.from_dict(self.image_processor_dict						,						size=42						,						crop_size=84						)
														self.assertEqual(image_processor.size						,						{"""height""": 42, """width""": 42}						)
														self.assertEqual(image_processor.crop_size						,						{"""height""": 84, """width""": 84}						)

							def  lowercase							(self						)  ->     int:
														# Initialize image_processing
														_snake_case     =						self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														_snake_case     =						prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCAmelCase						)

														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase						,						Image.Image						)

														# Test not batched input
														_snake_case     =						image_processing(image_inputs[0]						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)

														# Test batched
														_snake_case     =						image_processing(UpperCAmelCase						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)

							def  lowercase							(self						)  ->     Any:
														# Initialize image_processing
														_snake_case     =						self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														_snake_case     =						prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCAmelCase						,						numpify=UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase						,						np.ndarray						)

														# Test not batched input
														_snake_case     =						image_processing(image_inputs[0]						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)

														# Test batched
														_snake_case     =						image_processing(UpperCAmelCase						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)





							def  lowercase							(self						)  ->     List[Any]:
														# Initialize image_processing
														_snake_case     =						self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														_snake_case     =						prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCAmelCase						,						torchify=UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase						,						torch.Tensor						)

														# Test not batched input
														_snake_case     =						image_processing(image_inputs[0]						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)

														# Test batched
														_snake_case     =						image_processing(UpperCAmelCase						,						return_tensors="""pt"""						).pixel_values
														self.assertEqual(
														    encoded_images.shape						,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )						,						)

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__lowerCAmelCase  							= None


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase  							= {
    'facebook/xglm-564M': 2_048,
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		["input_ids", "attention_mask"]
							lowerCAmelCase_      		=		XGLMTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="<unk>"						,						UpperCAmelCase="<pad>"						,						**UpperCAmelCase						,						)  ->     List[Any]:
														# Compatibility with the original tokenizer
														_snake_case     =						7
														_snake_case     =						[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

														_snake_case     =						kwargs.get("""additional_special_tokens"""						,						[]						)
														kwargs["additional_special_tokens"] += [
														    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
														]

														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						**UpperCAmelCase						,						)

														_snake_case     =						vocab_file
														_snake_case     =						False if not self.vocab_file else True

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_snake_case     =						[self.sep_token_id]
														return sep + token_ids_a + sep + sep + token_ids_a

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:
														_snake_case     =						[self.sep_token_id]

														if token_ids_a is None:
																					return len(sep + token_ids_a						) * [0]
														return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""						)

														if not os.path.isdir(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCAmelCase						):
																					copyfile(self.vocab_file						,						UpperCAmelCase						)

														return (out_vocab_file,)