






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor
@require_vision
class     a    ( unittest.TestCase   ):



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	Any           =  tempfile.mkdtemp()

        __UpperCAmelCase							:	int           =  [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """want""",
            """##want""",
            """##ed""",
            """wa""",
            """un""",
            """runn""",
            """##ing""",
            """,""",
            """low""",
            """lowest""",
        ]
        __UpperCAmelCase							:	str           =  os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]					)
        with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""					) as vocab_writer:
          vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

        __UpperCAmelCase							:	Any           =  {
            """do_resize""": True,
            """size""": 20,
            """do_center_crop""": True,
            """crop_size""": 18,
            """do_normalize""": True,
            """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
            """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
        }
        __UpperCAmelCase							:	Any           =  os.path.join(self.tmpdirname		,      A_					)
        with open(self.image_processor_file		,      """w"""		,      encoding="""utf-8"""					) as fp:
          json.dump(A_		,      A_					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      **__lowercase					:			Union[str, Any]					)							->      Dict:
        return BertTokenizer.from_pretrained(self.tmpdirname		,      **A_					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname		,      **A_					)
      def 	UpperCAmelCase      (      self					:			Any		,      **__lowercase					:			List[Any]					)							->      Tuple:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname		,      **A_					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Any:
        shutil.rmtree(self.tmpdirname					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Tuple:
        __UpperCAmelCase							:	Optional[Any]           =  [np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta					)]
        __UpperCAmelCase							:	List[str]           =  [Image.fromarray(np.moveaxis(A_		,      0		,      -1					)					) for x in image_inputs]
        return image_inputs
      def 	UpperCAmelCase      (      self					:			str					)							->      Any:
        __UpperCAmelCase							:	Dict           =  self.get_tokenizer()
        __UpperCAmelCase							:	int           =  self.get_rust_tokenizer()
        __UpperCAmelCase							:	int           =  self.get_image_processor()

        __UpperCAmelCase							:	List[Any]           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)
        processor_slow.save_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	Dict           =  AlignProcessor.from_pretrained(self.tmpdirname		,      use_fast=A_					)

        __UpperCAmelCase							:	Optional[int]           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)
        processor_fast.save_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	List[Any]           =  AlignProcessor.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		,      A_					)
        self.assertIsInstance(processor_fast.tokenizer		,      A_					)

        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		,      A_					)
        self.assertIsInstance(processor_fast.image_processor		,      A_					)
      def 	UpperCAmelCase      (      self					:			Any					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Dict           =  AlignProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        __UpperCAmelCase							:	List[Any]           =  self.get_tokenizer(bos_token="""(BOS)"""		,      eos_token="""(EOS)"""					)
        __UpperCAmelCase							:	List[str]           =  self.get_image_processor(do_normalize=A_		,      padding_value=1.0					)

        __UpperCAmelCase							:	Union[str, Any]           =  AlignProcessor.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_					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	Any           =  self.get_image_processor()
        __UpperCAmelCase							:	str           =  self.get_tokenizer()

        __UpperCAmelCase							:	Any           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)

        __UpperCAmelCase							:	str           =  self.prepare_image_inputs()

        __UpperCAmelCase							:	Dict           =  image_processor(A_		,      return_tensors="""np"""					)
        __UpperCAmelCase							:	Any           =  processor(images=A_		,      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 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[Any]:
        __UpperCAmelCase							:	str           =  self.get_image_processor()
        __UpperCAmelCase							:	Dict           =  self.get_tokenizer()

        __UpperCAmelCase							:	Dict           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)

        __UpperCAmelCase							:	Union[str, Any]           =  """lower newer"""

        __UpperCAmelCase							:	Optional[int]           =  processor(text=A_					)

        __UpperCAmelCase							:	List[Any]           =  tokenizer(A_		,      padding="""max_length"""		,      max_length=64					)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]					)
      def 	UpperCAmelCase      (      self					:			str					)							->      List[Any]:
        __UpperCAmelCase							:	str           =  self.get_image_processor()
        __UpperCAmelCase							:	List[str]           =  self.get_tokenizer()

        __UpperCAmelCase							:	Optional[Any]           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)

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

        __UpperCAmelCase							:	str           =  processor(text=A_		,      images=A_					)

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

        # test if it raises when no input is passed
        with pytest.raises(A_					):
          processor()
      def 	UpperCAmelCase      (      self					:			str					)							->      Any:
        __UpperCAmelCase							:	int           =  self.get_image_processor()
        __UpperCAmelCase							:	int           =  self.get_tokenizer()

        __UpperCAmelCase							:	Optional[int]           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)

        __UpperCAmelCase							:	Optional[Any]           =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase							:	int           =  processor.batch_decode(A_					)
        __UpperCAmelCase							:	Dict           =  tokenizer.batch_decode(A_					)

        self.assertListEqual(A_		,      A_					)




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  self.get_image_processor()
        __UpperCAmelCase							:	Optional[Any]           =  self.get_tokenizer()

        __UpperCAmelCase							:	Any           =  AlignProcessor(tokenizer=A_		,      image_processor=A_					)

        __UpperCAmelCase							:	Union[str, Any]           =  """lower newer"""
        __UpperCAmelCase							:	Union[str, Any]           =  self.prepare_image_inputs()

        __UpperCAmelCase							:	Optional[Any]           =  processor(text=A_		,      images=A_					)

        self.assertListEqual(list(inputs.keys()					)		,      processor.model_input_names					)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
