
"""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				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=1_8				,    lowercase_=3_0				,    lowercase_=4_0_0				,    lowercase_=True				,    lowercase_=None				,    lowercase_=True				,    lowercase_=None				,    lowercase_=True				,    lowercase_=[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]				,    lowercase_=[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]				,    lowercase_=True				,    )  ->   Optional[Any]:
          UpperCAmelCase       =   parent
          UpperCAmelCase       =   batch_size
          UpperCAmelCase       =   num_channels
          UpperCAmelCase       =   image_size
          UpperCAmelCase       =   min_resolution
          UpperCAmelCase       =   max_resolution
          UpperCAmelCase       =   do_resize
          UpperCAmelCase       =   size if size is not None else {'height': 1_8, 'width': 1_8}
          UpperCAmelCase       =   do_center_crop
          UpperCAmelCase       =   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
          UpperCAmelCase       =   do_normalize
          UpperCAmelCase       =   image_mean
          UpperCAmelCase       =   image_std
          UpperCAmelCase       =   do_convert_rgb




      def       a_ (							self							)  ->   Optional[Any]:
          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							(						SCREAMING_SNAKE_CASE_       ,     unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							str							 =		OwlViTImageProcessor if is_vision_available() else None




      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   OwlViTImageProcessingTester(self							)




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




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   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'							)							)
          self.assertTrue(hasattr(lowercase_				,    'do_normalize'							)							)
          self.assertTrue(hasattr(lowercase_				,    'image_mean'							)							)
          self.assertTrue(hasattr(lowercase_				,    'image_std'							)							)
          self.assertTrue(hasattr(lowercase_				,    'do_convert_rgb'							)							)




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size				,    {'height': 1_8, 'width': 1_8}							)
          self.assertEqual(image_processor.crop_size				,    {'height': 1_8, 'width': 1_8}							)

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




      def       a_ (							self							)  ->   List[str]:
          # Initialize image_processing
          UpperCAmelCase       =   self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase       =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_							)

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

          # Test not batched input
          UpperCAmelCase       =   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       =   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       a_ (							self							)  ->   int:
          # Initialize image_processing
          UpperCAmelCase       =   self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase       =   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
          UpperCAmelCase       =   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       =   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       a_ (							self							)  ->   Optional[int]:
          # Initialize image_processing
          UpperCAmelCase       =   self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase       =   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
          UpperCAmelCase       =   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       =   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        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

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

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

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



