




"""simple docstring"""



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      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	List[str]				)				->							Dict:




     '''simple docstring'''

     A__	=    tempfile.mkdtemp()

     A__	=    [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     A__	=    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]				)				)

     A__	=    {
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
         "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
     }
     A__	=    os.path.join(self.tmpdirname     ,			snake_case_				)
     with open(self.image_processor_file     ,			"w"     ,			encoding="utf-8"				) as fp:
      json.dump(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			**snake_case_						:	List[Any]				)				->							str:




     '''simple docstring'''

     return BertTokenizer.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			**snake_case_						:	Tuple				)				->							Any:




     '''simple docstring'''

     return BertTokenizerFast.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    [np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta				)]
     A__	=    [Image.fromarray(np.moveaxis(snake_case_     ,			0     ,			-1				)				) for x in image_inputs]
     return image_inputs




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()
     A__	=    self.get_rust_tokenizer()
     A__	=    self.get_image_processor()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_slow.save_pretrained(self.tmpdirname				)
     A__	=    AlignProcessor.from_pretrained(self.tmpdirname     ,			use_fast=snake_case_				)

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_fast.save_pretrained(self.tmpdirname				)
     A__	=    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     ,			snake_case_				)
     self.assertIsInstance(processor_fast.tokenizer     ,			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     ,			snake_case_				)
     self.assertIsInstance(processor_fast.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    AlignProcessor(tokenizer=self.get_tokenizer()     ,			image_processor=self.get_image_processor()				)
     processor.save_pretrained(self.tmpdirname				)

     A__	=    self.get_tokenizer(bos_token="(BOS)"     ,			eos_token="(EOS)"				)
     A__	=    self.get_image_processor(do_normalize=snake_case_     ,			padding_value=1.0				)

     A__	=    AlignProcessor.from_pretrained(
         self.tmpdirname     ,			bos_token="(BOS)"     ,			eos_token="(EOS)"     ,			do_normalize=snake_case_     ,			padding_value=1.0				)

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

     self.assertEqual(processor.image_processor.to_json_string()     ,			image_processor_add_kwargs.to_json_string()				)
     self.assertIsInstance(processor.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    self.prepare_image_inputs()

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=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 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    "lower newer"

     A__	=    processor(text=snake_case_				)

     A__	=    tokenizer(snake_case_     ,			padding="max_length"     ,			max_length=64				)

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




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




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    "lower newer"
     A__	=    self.prepare_image_inputs()

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

     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(snake_case_				):
      processor()




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     A__	=    processor.batch_decode(snake_case_				)
     A__	=    tokenizer.batch_decode(snake_case_				)

     self.assertListEqual(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    "lower newer"
     A__	=    self.prepare_image_inputs()

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





