



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 SCREAMING_SNAKE_CASE__	(						unittest.TestCase       ):




    '''simple docstring'''






    def      _lowerCAmelCase   (			self						):
        A					:			str		=  tempfile.mkdtemp()

        A					:			List[str]		=  BlipImageProcessor()
        A					:			Union[str, Any]		=  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
        A					:			str		=  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

        A					:			Union[str, Any]		=  InstructBlipProcessor(__A,						__A,						__A						)

        processor.save_pretrained(self.tmpdirname						)






    def      _lowerCAmelCase   (			self,						**lowerCamelCase__						):
        return AutoProcessor.from_pretrained(self.tmpdirname,						**__A						).tokenizer






    def      _lowerCAmelCase   (			self,						**lowerCamelCase__						):
        return AutoProcessor.from_pretrained(self.tmpdirname,						**__A						).image_processor






    def      _lowerCAmelCase   (			self,						**lowerCamelCase__						):
        return AutoProcessor.from_pretrained(self.tmpdirname,						**__A						).qformer_tokenizer






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






    def      _lowerCAmelCase   (			self						):
        A					:			List[Any]		=  [np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)]

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

        return image_inputs






    def      _lowerCAmelCase   (			self						):
        A					:			Any		=  InstructBlipProcessor(
            tokenizer=self.get_tokenizer(),						image_processor=self.get_image_processor(),						qformer_tokenizer=self.get_qformer_tokenizer(),						)
        processor.save_pretrained(self.tmpdirname						)

        A					:			Optional[Any]		=  self.get_tokenizer(bos_token="""(BOS)""",						eos_token="""(EOS)"""						)
        A					:			Union[str, Any]		=  self.get_image_processor(do_normalize=__A,						padding_value=1.0						)

        A					:			Dict		=  InstructBlipProcessor.from_pretrained(
            self.tmpdirname,						bos_token="""(BOS)""",						eos_token="""(EOS)""",						do_normalize=__A,						padding_value=1.0						)

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

        self.assertEqual(processor.image_processor.to_json_string(),						image_processor_add_kwargs.to_json_string()						)
        self.assertIsInstance(processor.image_processor,						__A						)
        self.assertIsInstance(processor.qformer_tokenizer,						__A						)






    def      _lowerCAmelCase   (			self						):
        A					:			Optional[Any]		=  self.get_image_processor()
        A					:			List[str]		=  self.get_tokenizer()
        A					:			int		=  self.get_qformer_tokenizer()

        A					:			Optional[Any]		=  InstructBlipProcessor(
            tokenizer=__A,						image_processor=__A,						qformer_tokenizer=__A						)

        A					:			List[str]		=  self.prepare_image_inputs()

        A					:			Dict		=  image_processor(__A,						return_tensors="""np"""						)
        A					:			Dict		=  processor(images=__A,						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      _lowerCAmelCase   (			self						):
        A					:			Optional[Any]		=  self.get_image_processor()
        A					:			Any		=  self.get_tokenizer()
        A					:			Any		=  self.get_qformer_tokenizer()

        A					:			str		=  InstructBlipProcessor(
            tokenizer=__A,						image_processor=__A,						qformer_tokenizer=__A						)

        A					:			Dict		=  """lower newer"""

        A					:			Dict		=  processor(text=__A						)

        A					:			int		=  tokenizer(__A,						return_token_type_ids=__A						)
        A					:			Any		=  qformer_tokenizer(__A,						return_token_type_ids=__A						)

        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      _lowerCAmelCase   (			self						):
        A					:			Union[str, Any]		=  self.get_image_processor()
        A					:			Any		=  self.get_tokenizer()
        A					:			Optional[Any]		=  self.get_qformer_tokenizer()

        A					:			str		=  InstructBlipProcessor(
            tokenizer=__A,						image_processor=__A,						qformer_tokenizer=__A						)

        A					:			List[Any]		=  """lower newer"""
        A					:			int		=  self.prepare_image_inputs()

        A					:			Union[str, Any]		=  processor(text=__A,						images=__A						)

        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(__A						):
            processor()






    def      _lowerCAmelCase   (			self						):
        A					:			Dict		=  self.get_image_processor()
        A					:			Optional[Any]		=  self.get_tokenizer()
        A					:			List[Any]		=  self.get_qformer_tokenizer()

        A					:			Union[str, Any]		=  InstructBlipProcessor(
            tokenizer=__A,						image_processor=__A,						qformer_tokenizer=__A						)

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

        A					:			Dict		=  processor.batch_decode(__A						)
        A					:			Union[str, Any]		=  tokenizer.batch_decode(__A						)

        self.assertListEqual(__A,						__A						)






    def      _lowerCAmelCase   (			self						):
        A					:			Optional[int]		=  self.get_image_processor()
        A					:			Union[str, Any]		=  self.get_tokenizer()
        A					:			Tuple		=  self.get_qformer_tokenizer()

        A					:			Union[str, Any]		=  InstructBlipProcessor(
            tokenizer=__A,						image_processor=__A,						qformer_tokenizer=__A						)

        A					:			int		=  """lower newer"""
        A					:			Optional[int]		=  self.prepare_image_inputs()

        A					:			List[str]		=  processor(text=__A,						images=__A						)

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





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





