


from collections import OrderedDict
from typing import Mapping

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


a				:							Union[str, Any]					=			logging.get_logger(__name__)

a				:							Optional[int]					=			{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}

class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       _UpperCamelCase    		=      """squeezebert"""

       def __init__(			self	,						a__=3_05_22	,						a__=7_68	,						a__=12	,						a__=12	,						a__=30_72	,						a__="gelu"	,						a__=0.1	,						a__=0.1	,						a__=5_12	,						a__=2	,						a__=0.02	,						a__=1e-12	,						a__=0	,						a__=7_68	,						a__=4	,						a__=4	,						a__=4	,						a__=1	,						a__=4	,						a__=4	,						**a__	,						):






        '''simple docstring'''






        super().__init__(pad_token_id=a__	,						**a__					)

        snake_case   : List[Any]	  =      vocab_size
        snake_case   : Optional[Any]	  =      hidden_size
        snake_case   : str	  =      num_hidden_layers
        snake_case   : Optional[Any]	  =      num_attention_heads
        snake_case   : List[str]	  =      hidden_act
        snake_case   : Optional[int]	  =      intermediate_size
        snake_case   : Union[str, Any]	  =      hidden_dropout_prob
        snake_case   : List[Any]	  =      attention_probs_dropout_prob
        snake_case   : Optional[Any]	  =      max_position_embeddings
        snake_case   : Tuple	  =      type_vocab_size
        snake_case   : Optional[int]	  =      initializer_range
        snake_case   : Dict	  =      layer_norm_eps
        snake_case   : Tuple	  =      embedding_size
        snake_case   : Any	  =      q_groups
        snake_case   : List[Any]	  =      k_groups
        snake_case   : Optional[Any]	  =      v_groups
        snake_case   : Union[str, Any]	  =      post_attention_groups
        snake_case   : Tuple	  =      intermediate_groups
        snake_case   : Any	  =      output_groups







class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        if self.task == "multiple-choice":
         snake_case   : List[Any]	  =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
         snake_case   : Union[str, Any]	  =      {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
                ('''token_type_ids''', dynamic_axis),
            ]					)










import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor

class      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : str	  =      size if size is not None else {'''shortest_edge''': 20}
        snake_case   : List[str]	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        snake_case   : Optional[Any]	  =      parent
        snake_case   : Dict	  =      batch_size
        snake_case   : List[str]	  =      num_channels
        snake_case   : Union[str, Any]	  =      image_size
        snake_case   : Optional[Any]	  =      min_resolution
        snake_case   : Union[str, Any]	  =      max_resolution
        snake_case   : Optional[int]	  =      do_resize
        snake_case   : Optional[int]	  =      size
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      MobileNetVaImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      MobileNetVaImageProcessingTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''center_crop'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 20}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 18, '''width''': 18}					)

        snake_case   : Dict	  =      self.image_processing_class.from_dict(self.image_processor_dict	,						size=42	,						crop_size=84					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 42}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 84, '''width''': 84}					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						Image.Image					)

        # Test not batched input
        snake_case   : List[str]	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						numpify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						np.ndarray					)

        # Test not batched input
        snake_case   : Dict	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        snake_case   : List[Any]	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						torchify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						torch.Tensor					)

        # Test not batched input
        snake_case   : List[str]	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : Any	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)






