
"""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 MobileViTImageProcessor







class     lowercase(unittest.TestCase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=7     ,				__SCREAMING_SNAKE_CASE=3     ,				__SCREAMING_SNAKE_CASE=1_8     ,				__SCREAMING_SNAKE_CASE=3_0     ,				__SCREAMING_SNAKE_CASE=4_0_0     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=True     ,				)		->     Optional[Any]:







         """simple docstring"""



         a__		=			size if size is not None else {'shortest_edge': 2_0}
         a__		=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
         a__		=			parent
         a__		=			batch_size
         a__		=			num_channels
         a__		=			image_size
         a__		=			min_resolution
         a__		=			max_resolution
         a__		=			do_resize
         a__		=			size
         a__		=			do_center_crop
         a__		=			crop_size
         a__		=			do_flip_channel_order




     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



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







@require_torch
@require_vision
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Optional[Any]						=     MobileViTImageProcessor if is_vision_available() else None



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			MobileViTImageProcessingTester(self   )



     @property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()



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







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_resize'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'size'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'center_crop'   )   )
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,				'do_flip_channel_order'   )   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size     ,				{'shortest_edge': 2_0}   )
         self.assertEqual(image_processor.crop_size     ,				{'height': 1_8, 'width': 1_8}   )

         a__		=			self.image_processing_class.from_dict(self.image_processor_dict     ,				size=4_2     ,				crop_size=8_4   )
         self.assertEqual(image_processor.size     ,				{'shortest_edge': 4_2}   )
         self.assertEqual(image_processor.crop_size     ,				{'height': 8_4, 'width': 8_4}   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         a__		=			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				Image.Image   )

         # Test not batched input
         a__		=			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__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         a__		=			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=__SCREAMING_SNAKE_CASE     ,				numpify=__SCREAMING_SNAKE_CASE   )
         for image in image_inputs:
             self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				np.ndarray   )

         # Test not batched input
         a__		=			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__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



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

         # Test not batched input
         a__		=			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__		=			image_processing(__SCREAMING_SNAKE_CASE     ,				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     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


