

"""simple docstring"""





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      __a (   unittest.TestCase     ):

			"""simple docstring"""

			def __init__(				self  :       Union[str, Any]    ,	lowercase_  :       int    ,	lowercase_  :       Dict=7    ,	lowercase_  :       str=3    ,	lowercase_  :       str=18    ,	lowercase_  :       Union[str, Any]=30    ,	lowercase_  :       Tuple=400    ,	lowercase_  :       Optional[int]=True    ,	lowercase_  :       Any=None    ,	lowercase_  :       List[str]=True    ,	lowercase_  :       str=None    ,	):
								UpperCamelCase__		:     Any								=size if size is not None else {"shortest_edge": 20}
								UpperCamelCase__		:     Any								=crop_size if crop_size is not None else {"height": 18, "width": 18}
								UpperCamelCase__		:     str								=parent
								UpperCamelCase__		:     List[Any]								=batch_size
								UpperCamelCase__		:     Tuple								=num_channels
								UpperCamelCase__		:     Dict								=image_size
								UpperCamelCase__		:     Optional[Any]								=min_resolution
								UpperCamelCase__		:     Tuple								=max_resolution
								UpperCamelCase__		:     int								=do_resize
								UpperCamelCase__		:     Union[str, Any]								=size
								UpperCamelCase__		:     Tuple								=do_center_crop
								UpperCamelCase__		:     Tuple								=crop_size






			def  _lowerCAmelCase      (				self  :       Any    ):
								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      __a (   _a,     unittest.TestCase     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    MobileNetVaImageProcessor if is_vision_available() else None






			def  _lowerCAmelCase      (				self  :       Union[str, Any]    ):
								UpperCamelCase__		:     Optional[Any]								=MobileNetVaImageProcessingTester(self    )






			@property
			def  _lowerCAmelCase      (				self  :       int    ):
								return self.image_processor_tester.prepare_image_processor_dict()






			def  _lowerCAmelCase      (				self  :       int    ):
								UpperCamelCase__		:     Any								=self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(__UpperCAmelCase    ,	'''do_resize'''    )    )
								self.assertTrue(hasattr(__UpperCAmelCase    ,	'''size'''    )    )
								self.assertTrue(hasattr(__UpperCAmelCase    ,	'''do_center_crop'''    )    )
								self.assertTrue(hasattr(__UpperCAmelCase    ,	'''center_crop'''    )    )






			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								UpperCamelCase__		:     int								=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}    )

								UpperCamelCase__		:     List[Any]								=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  _lowerCAmelCase      (				self  :       Dict    ):
								pass






			def  _lowerCAmelCase      (				self  :       Optional[int]    ):
								# Initialize image_processing
								UpperCamelCase__		:     Optional[int]								=self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								UpperCamelCase__		:     List[Any]								=prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__UpperCAmelCase    )
								for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase    ,	Image.Image    )

								# Test not batched input
								UpperCamelCase__		:     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
								UpperCamelCase__		:     Optional[Any]								=image_processing(__UpperCAmelCase    ,	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  _lowerCAmelCase      (				self  :       str    ):
								# Initialize image_processing
								UpperCamelCase__		:     List[Any]								=self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								UpperCamelCase__		:     List[Any]								=prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__UpperCAmelCase    ,	numpify=__UpperCAmelCase    )
								for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase    ,	np.ndarray    )

								# Test not batched input
								UpperCamelCase__		:     List[Any]								=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
								UpperCamelCase__		:     List[str]								=image_processing(__UpperCAmelCase    ,	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  _lowerCAmelCase      (				self  :       int    ):
								# Initialize image_processing
								UpperCamelCase__		:     Union[str, Any]								=self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								UpperCamelCase__		:     Optional[Any]								=prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__UpperCAmelCase    ,	torchify=__UpperCAmelCase    )
								for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase    ,	torch.Tensor    )

								# Test not batched input
								UpperCamelCase__		:     Optional[int]								=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
								UpperCamelCase__		:     List[str]								=image_processing(__UpperCAmelCase    ,	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'''],
								    )    ,	)






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """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 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

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






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






