"""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   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=3       ,     lowercase=18       ,     lowercase=30       ,     lowercase=4_00       ,     lowercase=True       ,     lowercase=None       ,     lowercase=True       ,     lowercase=None       ,     )      ->     List[Any]:





        '''simple docstring'''





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



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





        '''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   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						MobileNetVaImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:	Dict     				=      MobileNetVaImageProcessingTester(self)



      @property
      def     lowerCamelCase_  (		self)      ->     Union[str, Any]:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	List[Any]     				=      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})

        a__:	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)      ->     List[Any]:





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





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

        # Test not batched input
        a__:	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
        a__:	int     				=      image_processing(lowercase       ,     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)      ->     Tuple:





        '''simple docstring'''





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

        # Test not batched input
        a__:	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
        a__:	int     				=      image_processing(lowercase       ,     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)      ->     Optional[int]:





        '''simple docstring'''





        a__:	int     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        a__:	Tuple     				=      prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=lowercase       ,     torchify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     torch.Tensor)

        # Test not batched input
        a__:	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
        a__:	Dict     				=      image_processing(lowercase       ,     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'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



