


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import (
     AutoProcessor,
     BertTokenizerFast,
     BlipImageProcessor,
     GPTaTokenizer,
     InstructBlipProcessor,
     PreTrainedTokenizerFast,
 )






@require_vision
class _lowerCamelCase(		unittest.TestCase     ):
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	tempfile.mkdtemp()

           _lowercase :							Optional[int]   	=	BlipImageProcessor()
           _lowercase :							str   	=	GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
           _lowercase :							int   	=	BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

           _lowercase :							Tuple   	=	InstructBlipProcessor(lowerCamelCase,   lowerCamelCase,   lowerCamelCase)

           processor.save_pretrained(self.tmpdirname)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           return AutoProcessor.from_pretrained(self.tmpdirname,   **lowerCamelCase).tokenizer
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           return AutoProcessor.from_pretrained(self.tmpdirname,   **lowerCamelCase).image_processor
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           return AutoProcessor.from_pretrained(self.tmpdirname,   **lowerCamelCase).qformer_tokenizer
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           shutil.rmtree(self.tmpdirname)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[str]   	=	[np.random.randint(2_55,   size=(3, 30, 4_00),   dtype=np.uinta)]

           _lowercase :							Any   	=	[Image.fromarray(np.moveaxis(lowerCamelCase,   0,   -1)) for x in image_inputs]

           return image_inputs
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Tuple   	=	InstructBlipProcessor(
               tokenizer=self.get_tokenizer(),   image_processor=self.get_image_processor(),   qformer_tokenizer=self.get_qformer_tokenizer(),   )
           processor.save_pretrained(self.tmpdirname)

           _lowercase :							List[Any]   	=	self.get_tokenizer(bos_token='(BOS)',   eos_token='(EOS)')
           _lowercase :							Optional[int]   	=	self.get_image_processor(do_normalize=lowerCamelCase,   padding_value=1.0)

           _lowercase :							Dict   	=	InstructBlipProcessor.from_pretrained(
               self.tmpdirname,   bos_token='(BOS)',   eos_token='(EOS)',   do_normalize=lowerCamelCase,   padding_value=1.0)

           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)
           self.assertIsInstance(processor.qformer_tokenizer,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.get_image_processor()
           _lowercase :							Optional[int]   	=	self.get_tokenizer()
           _lowercase :							List[str]   	=	self.get_qformer_tokenizer()

           _lowercase :							Union[str, Any]   	=	InstructBlipProcessor(
               tokenizer=lowerCamelCase,   image_processor=lowerCamelCase,   qformer_tokenizer=lowerCamelCase)

           _lowercase :							str   	=	self.prepare_image_inputs()

           _lowercase :							Dict   	=	image_processor(lowerCamelCase,   return_tensors='np')
           _lowercase :							Tuple   	=	processor(images=lowerCamelCase,   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     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Dict   	=	self.get_image_processor()
           _lowercase :							List[str]   	=	self.get_tokenizer()
           _lowercase :							List[Any]   	=	self.get_qformer_tokenizer()

           _lowercase :							Optional[Any]   	=	InstructBlipProcessor(
               tokenizer=lowerCamelCase,   image_processor=lowerCamelCase,   qformer_tokenizer=lowerCamelCase)

           _lowercase :							List[str]   	=	'lower newer'

           _lowercase :							int   	=	processor(text=lowerCamelCase)

           _lowercase :							Tuple   	=	tokenizer(lowerCamelCase,   return_token_type_ids=lowerCamelCase)
           _lowercase :							Any   	=	qformer_tokenizer(lowerCamelCase,   return_token_type_ids=lowerCamelCase)

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

           for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key],   encoded_processor['qformer_' + key])
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.get_image_processor()
           _lowercase :							int   	=	self.get_tokenizer()
           _lowercase :							int   	=	self.get_qformer_tokenizer()

           _lowercase :							Tuple   	=	InstructBlipProcessor(
               tokenizer=lowerCamelCase,   image_processor=lowerCamelCase,   qformer_tokenizer=lowerCamelCase)

           _lowercase :							str   	=	'lower newer'
           _lowercase :							int   	=	self.prepare_image_inputs()

           _lowercase :							Union[str, Any]   	=	processor(text=lowerCamelCase,   images=lowerCamelCase)

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

           # test if it raises when no input is passed
           with pytest.raises(lowerCamelCase):
               processor()
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.get_image_processor()
           _lowercase :							Tuple   	=	self.get_tokenizer()
           _lowercase :							Dict   	=	self.get_qformer_tokenizer()

           _lowercase :							List[str]   	=	InstructBlipProcessor(
               tokenizer=lowerCamelCase,   image_processor=lowerCamelCase,   qformer_tokenizer=lowerCamelCase)

           _lowercase :							Tuple   	=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _lowercase :							Dict   	=	processor.batch_decode(lowerCamelCase)
           _lowercase :							Tuple   	=	tokenizer.batch_decode(lowerCamelCase)

           self.assertListEqual(lowerCamelCase,   lowerCamelCase)




       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.get_image_processor()
           _lowercase :							str   	=	self.get_tokenizer()
           _lowercase :							Dict   	=	self.get_qformer_tokenizer()

           _lowercase :							Union[str, Any]   	=	InstructBlipProcessor(
               tokenizer=lowerCamelCase,   image_processor=lowerCamelCase,   qformer_tokenizer=lowerCamelCase)

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

           _lowercase :							Tuple   	=	processor(text=lowerCamelCase,   images=lowerCamelCase)

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






import pytest


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"__dummy_dataset1__"

SCREAMING_SNAKE_CASE :   int							      =				"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"



@pytest.fixture
def        UpperCamelCase_(   )					->		Dict:
    return DATASET_LOADING_SCRIPT_NAME



@pytest.fixture
def        UpperCamelCase_(   )					->		List[Any]:
    return DATASET_LOADING_SCRIPT_CODE



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	dataset_loading_script_name
    _lowercase :							Union[str, Any]   	=	tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	script_dir / F'''{script_name}.py'''
    with open(lowerCamelCase_							,				'w'				) as f:
        f.write(lowerCamelCase_				)
    return str(lowerCamelCase_				)


