






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					,UpperCamelCase					,UpperCamelCase=7					,UpperCamelCase=3					,UpperCamelCase=18					,UpperCamelCase=30					,UpperCamelCase=400					,UpperCamelCase=True					,UpperCamelCase=None					,UpperCamelCase=True					,UpperCamelCase=None					,)						->    Optional[Any]:
					snake_case__      :Optional[Any]         =   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__      :Union[str, Any]         =   parent
					snake_case__      :Union[str, Any]         =   batch_size
					snake_case__      :Optional[Any]         =   num_channels
					snake_case__      :List[str]         =   image_size
					snake_case__      :List[str]         =   min_resolution
					snake_case__      :Tuple         =   max_resolution
					snake_case__      :Optional[Any]         =   do_resize
					snake_case__      :Any         =   size
					snake_case__      :Dict         =   do_center_crop
					snake_case__      :int         =   crop_size






	def    lowerCAmelCase_							(		self       )						->    Dict:
					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						(			_A       ,    unittest.TestCase ):
	_A          =   MobileNetVaImageProcessor if is_vision_available() else None






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :List[Any]         =   MobileNetVaImageProcessingTester(self       )






	@property
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :List[str]         =   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					,"crop_size"       )       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[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__      :Optional[int]         =   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       )						->    Optional[int]:
					pass






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					# Initialize image_processing
					snake_case__      :Tuple         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case__      :int         =   prepare_image_inputs(self.image_processor_tester					,equal_resolution=UpperCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase					,Image.Image       )

					# Test not batched input
					snake_case__      :Optional[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
					snake_case__      :Union[str, 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       )						->    List[str]:
					# Initialize image_processing
					snake_case__      :Union[str, Any]         =   self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					snake_case__      :Optional[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
					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(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       )						->    Tuple:
					# Initialize image_processing
					snake_case__      :Tuple         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					snake_case__      :Union[str, 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
					snake_case__      :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
					snake_case__      :List[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"],
					    )					,)







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 					_snake_case						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask