"""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 OwlViTImageProcessor






class    __UpperCAmelCase						(	unittest.TestCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=7	, _lowerCamelCase=3	, _lowerCamelCase=18	, _lowerCamelCase=30	, _lowerCamelCase=400	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=True	, _lowerCamelCase=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	, _lowerCamelCase=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	, _lowerCamelCase=True	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=num_channels
										lowerCamelCase__					=image_size
										lowerCamelCase__					=min_resolution
										lowerCamelCase__					=max_resolution
										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean
										lowerCamelCase__					=image_std
										lowerCamelCase__					=do_convert_rgb






				def 		_a	(      self      ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Tuple  							=       OwlViTImageProcessor if is_vision_available() else None

				def 		_a	(      self      ):
										lowerCamelCase__					=OwlViTImageProcessingTester(self      )

				@property
				def 		_a	(      self      ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_resize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "size"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_center_crop"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "center_crop"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_normalize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_mean"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_std"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_convert_rgb"      )      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size	, {"height": 18, "width": 18}      )
										self.assertEqual(image_processor.crop_size	, {"height": 18, "width": 18}      )

										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict	, size=42	, crop_size=84      )
										self.assertEqual(image_processor.size	, {"height": 42, "width": 42}      )
										self.assertEqual(image_processor.crop_size	, {"height": 84, "width": 84}      )

				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase      )

										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, Image.Image      )

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, numpify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, np.ndarray      )

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, torchify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, torch.Tensor      )

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

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

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__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 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.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





