'''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   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	int = 32      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Union[int, float] = 1 / 2_55      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = [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]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = [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]      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	str=7      ,       SCREAMING_SNAKE_CASE__   :	Dict=30      ,       SCREAMING_SNAKE_CASE__   :	Dict=4_00      ,       SCREAMING_SNAKE_CASE__   :	int=3      ,       )  ->    List[str]:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						size if size is not None else {"""shortest_edge""": 2_88}
  __lowerCAmelCase											=						size_divisor
  __lowerCAmelCase											=						do_rescale
  __lowerCAmelCase											=						rescale_factor
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						do_center_crop
  __lowerCAmelCase											=						image_mean
  __lowerCAmelCase											=						image_std
  __lowerCAmelCase											=						do_pad
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						min_resolution
  __lowerCAmelCase											=						max_resolution
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  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 a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    int:
  if not batched:
   __lowerCAmelCase											=						self.size["""shortest_edge"""]
   __lowerCAmelCase											=						image_inputs[0]
   if isinstance(SCREAMING_SNAKE_CASE__      ,       Image.Image							):
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.size
   else:
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.shape[1], image.shape[2]
   __lowerCAmelCase											=						size / min(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
   if h < w:
    __lowerCAmelCase							,       __lowerCAmelCase											=						size, scale * w
   else:
    __lowerCAmelCase							,       __lowerCAmelCase											=						scale * h, size

   __lowerCAmelCase											=						int((13_33 / 8_00) * size							)
   if max(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) > max_size:
    __lowerCAmelCase											=						max_size / max(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase											=						newh * scale
    __lowerCAmelCase											=						neww * scale

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

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

  return expected_height, expected_width







@require_torch
@require_vision
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					BridgeTowerImageProcessor if is_vision_available() else None
 def a					(      self   :	Any							)  ->    int:
  __lowerCAmelCase											=						BridgeTowerImageProcessingTester(self							)
 @property
 def a					(      self   :	Optional[Any]							)  ->    List[Any]:
  return self.image_processor_tester.prepare_image_processor_dict()
 def a					(      self   :	Optional[int]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_mean"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_std"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_normalize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_resize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """size"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """size_divisor"""							)							)
 def a					(      self   :	Dict							)  ->    Optional[int]:
  pass
 def a					(      self   :	int							)  ->    List[Any]:
  # Initialize image processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PIL images
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase											=						image_processing(SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)
  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	List[str]							)  ->    Union[str, Any]:
  # Initialize image processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random numpy tensors
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase											=						image_processing(SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).pixel_values

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





 def a					(      self   :	int							)  ->    Optional[Any]:
  # Initialize image processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PyTorch tensors
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase											=						image_processing(SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).pixel_values

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







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:      int					      =				logging.get_logger(__name__)

_A			:      Any					      =				{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""ctrl"""
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["""past_key_values"""]
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=24_65_34      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2_56      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12_80      ,       SCREAMING_SNAKE_CASE__   :	str=81_92      ,       SCREAMING_SNAKE_CASE__   :	List[str]=48      ,       SCREAMING_SNAKE_CASE__   :	Tuple=16      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       )  ->    Tuple:
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						n_positions
  __lowerCAmelCase											=						n_embd
  __lowerCAmelCase											=						n_layer
  __lowerCAmelCase											=						n_head
  __lowerCAmelCase											=						dff
  __lowerCAmelCase											=						resid_pdrop
  __lowerCAmelCase											=						embd_pdrop
  __lowerCAmelCase											=						layer_norm_epsilon
  __lowerCAmelCase											=						initializer_range

  __lowerCAmelCase											=						use_cache

  super().__init__(**SCREAMING_SNAKE_CASE__							)






