


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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[Any]:
             a_ :		List[str]							=  tempfile.mkdtemp()

             # fmt: off
             a_ :		List[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
             a_ :		Tuple							=  dict(zip(SCREAMING_SNAKE_CASE__					,							range(len(SCREAMING_SNAKE_CASE__					)					)					)					)
             a_ :		Optional[Any]							=  ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
             a_ :		Any							=  {'unk_token': '<unk>'}

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

             a_ :		Tuple							=  {
                 'do_resize': True,
                 'size': 2_0,
                 'do_center_crop': True,
                 'crop_size': 1_8,
                 'do_normalize': True,
                 'image_mean': [0.48145466, 0.4578275, 0.40821073],
                 'image_std': [0.26862954, 0.26130258, 0.27577711],
             }
             a_ :		Any							=  os.path.join(self.tmpdirname					,							SCREAMING_SNAKE_CASE__					)
             with open(self.image_processor_file					,							'w'					,							encoding='utf-8'					) as fp:
                    json.dump(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							**SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Optional[int]:
             return CLIPTokenizer.from_pretrained(self.tmpdirname					,							pad_token='!'					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      Any:
             return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,							pad_token='!'					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Optional[int]:
             return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Dict:
             shutil.rmtree(self.tmpdirname					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[str]:
             a_ :		Optional[int]							=  [np.random.randint(2_5_5					,							size=(3, 3_0, 4_0_0)					,							dtype=np.uinta					)]

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

             return image_inputs
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Optional[int]							=  self.get_tokenizer()
             a_ :		int							=  self.get_rust_tokenizer()
             a_ :		Optional[int]							=  self.get_image_processor()

             a_ :		List[Any]							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)
             processor_slow.save_pretrained(self.tmpdirname					)
             a_ :		Optional[int]							=  OwlViTProcessor.from_pretrained(self.tmpdirname					,							use_fast=SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)
             processor_fast.save_pretrained(self.tmpdirname					)
             a_ :		Optional[Any]							=  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					,							SCREAMING_SNAKE_CASE__					)
             self.assertIsInstance(processor_fast.tokenizer					,							SCREAMING_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					,							SCREAMING_SNAKE_CASE__					)
             self.assertIsInstance(processor_fast.image_processor					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      int:
             a_ :		Optional[int]							=  OwlViTProcessor(tokenizer=self.get_tokenizer()					,							image_processor=self.get_image_processor()					)
             processor.save_pretrained(self.tmpdirname					)

             a_ :		Dict							=  self.get_tokenizer(bos_token='(BOS)'					,							eos_token='(EOS)'					)
             a_ :		Tuple							=  self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  OwlViTProcessor.from_pretrained(
                 self.tmpdirname					,							bos_token='(BOS)'					,							eos_token='(EOS)'					,							do_normalize=SCREAMING_SNAKE_CASE__					)

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

             self.assertEqual(processor.image_processor.to_json_string()					,							image_processor_add_kwargs.to_json_string()					)
             self.assertIsInstance(processor.image_processor					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Optional[Any]:
             a_ :		int							=  self.get_image_processor()
             a_ :		str							=  self.get_tokenizer()

             a_ :		List[str]							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  self.prepare_image_inputs()

             a_ :		Tuple							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					)
             a_ :		List[str]							=  processor(images=SCREAMING_SNAKE_CASE__					,							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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Dict:
             a_ :		List[Any]							=  self.get_image_processor()
             a_ :		Tuple							=  self.get_tokenizer()

             a_ :		str							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  'lower newer'

             a_ :		Dict							=  processor(text=SCREAMING_SNAKE_CASE__					,							return_tensors='np'					)

             a_ :		str							=  tokenizer(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					)

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

             a_ :		Tuple							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  'lower newer'
             a_ :		Union[str, Any]							=  self.prepare_image_inputs()

             a_ :		int							=  processor(text=SCREAMING_SNAKE_CASE__					,							images=SCREAMING_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(SCREAMING_SNAKE_CASE__					):
                    processor()
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             a_ :		List[Any]							=  'google/owlvit-base-patch32'
             a_ :		Optional[Any]							=  OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[Any]							=  ['cat', 'nasa badge']
             a_ :		Optional[int]							=  processor(text=SCREAMING_SNAKE_CASE__					)

             a_ :		Dict							=  1_6
             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(SCREAMING_SNAKE_CASE__					):
                    processor()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		Optional[int]							=  'google/owlvit-base-patch32'
             a_ :		Optional[int]							=  OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  [['cat', 'nasa badge'], ['person']]
             a_ :		List[str]							=  processor(text=SCREAMING_SNAKE_CASE__					)

             a_ :		str							=  1_6
             a_ :		Optional[int]							=  len(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  max([len(SCREAMING_SNAKE_CASE__					) 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(SCREAMING_SNAKE_CASE__					):
                    processor()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[Any]:
             a_ :		Optional[int]							=  'google/owlvit-base-patch32'
             a_ :		int							=  OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		Any							=  ['cat', 'nasa badge']
             a_ :		str							=  processor(text=SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  1_6
             a_ :		int							=  inputs['input_ids']
             a_ :		Dict							=  [
                 [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 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 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             a_ :		int							=  self.get_image_processor()
             a_ :		Tuple							=  self.get_tokenizer()

             a_ :		Optional[Any]							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  self.prepare_image_inputs()
             a_ :		int							=  self.prepare_image_inputs()

             a_ :		Optional[Any]							=  processor(images=SCREAMING_SNAKE_CASE__					,							query_images=SCREAMING_SNAKE_CASE__					)

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

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Optional[Any]:
             a_ :		int							=  self.get_image_processor()
             a_ :		Union[str, Any]							=  self.get_tokenizer()

             a_ :		Any							=  OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,							image_processor=SCREAMING_SNAKE_CASE__					)

             a_ :		str							=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             a_ :		Optional[Any]							=  processor.batch_decode(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)






from typing import TYPE_CHECKING

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


UpperCAmelCase_				:			Optional[Any]   =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_				:			Union[str, Any]   =	_LazyModule(__name__, globals()['__file__'], _import_structure)


