import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      UpperCamelCase													=		False

if is_vision_available():
      from PIL import Image

      from transformers import (
          AutoProcessor,
          PixaStructImageProcessor,
          PixaStructProcessor,
          PreTrainedTokenizerFast,
          TaTokenizer,
      )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."						,)
@require_vision
@require_torch
class 						snake_case_ (	unittest.TestCase ):






   def    __UpperCamelCase  (   self       :  Any						)      ->		Any:
       lowercase__				:     Optional[int]					      =       tempfile.mkdtemp()

       lowercase__				:     Any					      =       PixaStructImageProcessor()
       lowercase__				:     Union[str, Any]					      =       TaTokenizer.from_pretrained("t5-small"						)

       lowercase__				:     Optional[int]					      =       PixaStructProcessor(lowercase_			,		lowercase_						)

       processor.save_pretrained(self.tmpdirname						)






   def    __UpperCamelCase  (   self       :  Any			,		**lowercase_       :  int						)      ->		int:
       return AutoProcessor.from_pretrained(self.tmpdirname			,		**lowercase_						).tokenizer






   def    __UpperCamelCase  (   self       :  Dict			,		**lowercase_       :  Any						)      ->		Dict:
       return AutoProcessor.from_pretrained(self.tmpdirname			,		**lowercase_						).image_processor






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Any:
       shutil.rmtree(self.tmpdirname						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Dict:
       lowercase__				:     Tuple					      =       [np.random.randint(2_55			,		size=(3, 30, 4_00)			,		dtype=np.uinta						)]

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

       return image_inputs






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[str]:
       lowercase__				:     Any					      =       PixaStructProcessor(tokenizer=self.get_tokenizer()			,		image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       lowercase__				:     Optional[Any]					      =       self.get_tokenizer(bos_token="(BOS)"			,		eos_token="(EOS)"						)
       lowercase__				:     Optional[int]					      =       self.get_image_processor(do_normalize=lowercase_			,		padding_value=1.0						)

       lowercase__				:     Optional[int]					      =       PixaStructProcessor.from_pretrained(
           self.tmpdirname			,		bos_token="(BOS)"			,		eos_token="(EOS)"			,		do_normalize=lowercase_			,		padding_value=1.0						)

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

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






   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[Any]:
       lowercase__				:     int					      =       self.get_image_processor()
       lowercase__				:     Union[str, Any]					      =       self.get_tokenizer()

       lowercase__				:     Tuple					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

       lowercase__				:     Optional[Any]					      =       self.prepare_image_inputs()

       lowercase__				:     str					      =       image_processor(lowercase_			,		return_tensors="np"						)
       lowercase__				:     List[str]					      =       processor(images=lowercase_			,		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       :  int						)      ->		Optional[Any]:
       lowercase__				:     List[str]					      =       self.get_image_processor()
       lowercase__				:     str					      =       self.get_tokenizer()

       lowercase__				:     List[Any]					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

       lowercase__				:     Dict					      =       "lower newer"

       lowercase__				:     Tuple					      =       processor(text=lowercase_						)

       lowercase__				:     str					      =       tokenizer(lowercase_			,		return_token_type_ids=lowercase_			,		add_special_tokens=lowercase_						)

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






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Tuple:
       lowercase__				:     List[Any]					      =       self.get_image_processor()
       lowercase__				:     Any					      =       self.get_tokenizer()

       lowercase__				:     Tuple					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

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

       lowercase__				:     List[Any]					      =       processor(text=lowercase_			,		images=lowercase_						)

       self.assertListEqual(
           list(inputs.keys()						)			,		["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

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






   def    __UpperCamelCase  (   self       :  Any						)      ->		Tuple:
       lowercase__				:     Any					      =       self.get_image_processor()
       lowercase__				:     Tuple					      =       self.get_tokenizer()

       lowercase__				:     Any					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

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

       lowercase__				:     int					      =       processor(text=lowercase_			,		images=lowercase_						)

       lowercase__				:     Union[str, Any]					      =       [5_12, 10_24, 20_48, 40_96]
       lowercase__				:     Union[str, Any]					      =       [7_70, 7_70, 7_70, 7_70]
       # with text
       for i, max_patch in enumerate(lowercase_						):
           lowercase__				:     Union[str, Any]					      =       processor(text=lowercase_			,		images=lowercase_			,		max_patches=lowercase_						)
           self.assertEqual(inputs["flattened_patches"][0].shape[0]			,		lowercase_						)
           self.assertEqual(inputs["flattened_patches"][0].shape[1]			,		expected_hidden_size[i]						)

       # without text input
       for i, max_patch in enumerate(lowercase_						):
           lowercase__				:     List[Any]					      =       processor(images=lowercase_			,		max_patches=lowercase_						)
           self.assertEqual(inputs["flattened_patches"][0].shape[0]			,		lowercase_						)
           self.assertEqual(inputs["flattened_patches"][0].shape[1]			,		expected_hidden_size[i]						)






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Dict:
       lowercase__				:     Any					      =       self.get_image_processor()
       lowercase__				:     int					      =       self.get_tokenizer()

       lowercase__				:     str					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

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

       lowercase__				:     List[Any]					      =       processor.batch_decode(lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.batch_decode(lowercase_						)

       self.assertListEqual(lowercase_			,		lowercase_						)







   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Any:
       lowercase__				:     str					      =       self.get_image_processor()
       lowercase__				:     Dict					      =       self.get_tokenizer()

       lowercase__				:     int					      =       PixaStructProcessor(tokenizer=lowercase_			,		image_processor=lowercase_						)

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

       lowercase__				:     Optional[int]					      =       processor(text=lowercase_			,		images=lowercase_						)

       # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
       self.assertListEqual(
           list(inputs.keys()						)			,		["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

       lowercase__				:     Dict					      =       processor(text=lowercase_						)

       # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
       self.assertListEqual(list(inputs.keys()						)			,		["input_ids", "attention_mask"]						)




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 						snake_case_ (	__A ):
   __A			:	Any        =       "mobilevit"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[Any]=3			,		lowercase_       :  List[Any]=2_56			,		lowercase_       :  Union[str, Any]=2			,		lowercase_       :  int=[1_44, 1_92, 2_40]			,		lowercase_       :  List[str]=[16, 32, 64, 96, 1_28, 1_60, 6_40]			,		lowercase_       :  Tuple=4			,		lowercase_       :  Optional[int]=2.0			,		lowercase_       :  int=4.0			,		lowercase_       :  str="silu"			,		lowercase_       :  Optional[Any]=3			,		lowercase_       :  Dict=32			,		lowercase_       :  Union[str, Any]=0.1			,		lowercase_       :  str=0.0			,		lowercase_       :  str=0.1			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Union[str, Any]=1E-5			,		lowercase_       :  Tuple=True			,		lowercase_       :  Optional[Any]=2_56			,		lowercase_       :  List[str]=[6, 12, 18]			,		lowercase_       :  Any=0.1			,		lowercase_       :  List[str]=2_55			,		**lowercase_       :  Tuple			,		)      ->		Optional[Any]:
       super().__init__(**lowercase_						)

       lowercase__				:     List[str]					      =       num_channels
       lowercase__				:     Optional[int]					      =       image_size
       lowercase__				:     Any					      =       patch_size
       lowercase__				:     int					      =       hidden_sizes
       lowercase__				:     str					      =       neck_hidden_sizes
       lowercase__				:     Optional[int]					      =       num_attention_heads
       lowercase__				:     Tuple					      =       mlp_ratio
       lowercase__				:     Tuple					      =       expand_ratio
       lowercase__				:     List[str]					      =       hidden_act
       lowercase__				:     Optional[int]					      =       conv_kernel_size
       lowercase__				:     Union[str, Any]					      =       output_stride
       lowercase__				:     Optional[int]					      =       hidden_dropout_prob
       lowercase__				:     List[str]					      =       attention_probs_dropout_prob
       lowercase__				:     Union[str, Any]					      =       classifier_dropout_prob
       lowercase__				:     Union[str, Any]					      =       initializer_range
       lowercase__				:     List[str]					      =       layer_norm_eps
       lowercase__				:     Dict					      =       qkv_bias

       # decode head attributes for semantic segmentation
       lowercase__				:     List[Any]					      =       aspp_out_channels
       lowercase__				:     List[Any]					      =       atrous_rates
       lowercase__				:     str					      =       aspp_dropout_prob
       lowercase__				:     int					      =       semantic_loss_ignore_index



class 						snake_case_ (	__A ):
   __A			:	int        =       version.parse("1.11" )






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Mapping[str, Mapping[int, str]]:
       return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]						)






   @property
   def    __UpperCamelCase  (   self       :  str						)      ->		Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([("logits", {0: "batch"})]						)
       else:
           return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)







   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		float:
       return 1E-4



