

'''simple docstring'''




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ = True ,			a_ = None ,			a_ = 3_2 ,			a_ = True ,			a_ = 1 / 2_5_5 ,			a_ = True ,			a_ = True ,			a_ = [0.4814_5466, 0.457_8275, 0.4082_1073] ,			a_ = [0.2686_2954, 0.2613_0258, 0.2757_7711] ,			a_ = True ,			a_=7 ,			a_=3_0 ,			a_=4_0_0 ,			a_=3 ,			)				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						do_resize
    UpperCAmelCase    				=						size if size is not None else {'shortest_edge': 2_8_8}
    UpperCAmelCase    				=						size_divisor
    UpperCAmelCase    				=						do_rescale
    UpperCAmelCase    				=						rescale_factor
    UpperCAmelCase    				=						do_normalize
    UpperCAmelCase    				=						do_center_crop
    UpperCAmelCase    				=						image_mean
    UpperCAmelCase    				=						image_std
    UpperCAmelCase    				=						do_pad
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						min_resolution
    UpperCAmelCase    				=						max_resolution
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }





  def     snake_case_      (  self ,			a_ ,			a_=False  )				->					str:


    """simple docstring"""
    if not batched:
      UpperCAmelCase    				=						self.size['shortest_edge']
      UpperCAmelCase    				=						image_inputs[0]
      if isinstance(a_ ,			Image.Image  ):
        UpperCAmelCase				,   UpperCAmelCase    				=						image.size
      else:
        UpperCAmelCase				,   UpperCAmelCase    				=						image.shape[1], image.shape[2]
      UpperCAmelCase    				=						size / min(a_ ,			a_  )
      if h < w:
        UpperCAmelCase				,   UpperCAmelCase    				=						size, scale * w
      else:
        UpperCAmelCase				,   UpperCAmelCase    				=						scale * h, size

      UpperCAmelCase    				=						int((1_3_3_3 / 8_0_0) * size  )
      if max(a_ ,			a_  ) > max_size:
        UpperCAmelCase    				=						max_size / max(a_ ,			a_  )
        UpperCAmelCase    				=						newh * scale
        UpperCAmelCase    				=						neww * scale

      UpperCAmelCase				,   UpperCAmelCase    				=						int(newh + 0.5  ), int(neww + 0.5  )
      UpperCAmelCase				,   UpperCAmelCase    				=						(
          newh // self.size_divisor * self.size_divisor,
          neww // self.size_divisor * self.size_divisor,
      )

    else:
      UpperCAmelCase    				=						[]
      for image in image_inputs:
        UpperCAmelCase				,   UpperCAmelCase    				=						self.get_expected_values([image]  )
        expected_values.append((expected_height, expected_width)  )
      UpperCAmelCase    				=						max(a_ ,			key=lambda a_  : item[0]  )[0]
      UpperCAmelCase    				=						max(a_ ,			key=lambda a_  : item[1]  )[1]

    return expected_height, expected_width




@require_torch
@require_vision
class 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= BridgeTowerImageProcessor if is_vision_available() else None
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						BridgeTowerImageProcessingTester(self  )
  @property
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(a_ ,			'image_mean'  )  )
    self.assertTrue(hasattr(a_ ,			'image_std'  )  )
    self.assertTrue(hasattr(a_ ,			'do_normalize'  )  )
    self.assertTrue(hasattr(a_ ,			'do_resize'  )  )
    self.assertTrue(hasattr(a_ ,			'size'  )  )
    self.assertTrue(hasattr(a_ ,			'size_divisor'  )  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    pass
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    # Initialize image processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=a_  )
    for image in image_inputs:
      self.assertIsInstance(a_ ,			Image.Image  )

    # Test not batched input
    UpperCAmelCase    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_  )
    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						image_processing(a_ ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_ ,			batched=a_  )
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    # Initialize image processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=a_ ,			numpify=a_  )
    for image in image_inputs:
      self.assertIsInstance(a_ ,			np.ndarray  )

    # Test not batched input
    UpperCAmelCase    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_  )
    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						image_processing(a_ ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_ ,			batched=a_  )
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)





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


    """simple docstring"""
    # Initialize image processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=a_ ,			torchify=a_  )
    for image in image_inputs:
      self.assertIsInstance(a_ ,			torch.Tensor  )

    # Test not batched input
    UpperCAmelCase    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_  )
    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						image_processing(a_ ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_ ,			batched=a_  )
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)





'''simple docstring'''




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							lowercase_						(		nn.Module			):

  '''simple docstring'''





  def __init__(  self ,			a_ = 1_6 ,			a_ = 8_8 ,			a_ = None ,			a_ = 1 ,			a_ = 0.0 ,			a_ = 3_2 ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = "geglu" ,			a_ = None ,			)				->					List[str]:


    """simple docstring"""
    super().__init__()
    UpperCAmelCase    				=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=a_ ,			attention_head_dim=a_ ,			in_channels=a_ ,			num_layers=a_ ,			dropout=a_ ,			norm_num_groups=a_ ,			cross_attention_dim=a_ ,			attention_bias=a_ ,			sample_size=a_ ,			num_vector_embeds=a_ ,			activation_fn=a_ ,			num_embeds_ada_norm=a_ ,			)
            for _ in range(2  )
        ]  )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase    				=						0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase    				=						[7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase    				=						[1, 0]





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=None ,			a_=None ,			a_ = True ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_states

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						0
    # attention_mask is not used yet
    for i in range(2  ):
      # for each of the two transformers, pass the corresponding condition tokens
      UpperCAmelCase    				=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      UpperCAmelCase    				=						self.transformer_index_for_condition[i]
      UpperCAmelCase    				=						self.transformers[transformer_index](
          a_ ,			encoder_hidden_states=a_ ,			timestep=a_ ,			cross_attention_kwargs=a_ ,			return_dict=a_ ,			)[0]
      encoded_states.append(encoded_state - input_states  )
      tokens_start += self.condition_lengths[i]

    UpperCAmelCase    				=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase    				=						output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=a_  )


