
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
				import PIL

# soft dependency
if is_pytesseract_available():
				import pytesseract

__lowerCAmelCase												=	logging.get_logger(__name__)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							return [
							    int(1000 * (box[0] / width)				),
							    int(1000 * (box[1] / height)				),
							    int(1000 * (box[2] / width)				),
							    int(1000 * (box[3] / height)				),
							]






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = None				):
							_snake_case       =  tesseract_config if tesseract_config is not None else ""

							# apply OCR
							_snake_case       =  to_pil_image(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  pil_image.size
							_snake_case       =  pytesseract.image_to_data(_SCREAMING_SNAKE_CASE							, lang=_SCREAMING_SNAKE_CASE							, output_type="""dict"""							, config=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  data["text"], data["left"], data["top"], data["width"], data["height"]

							# filter empty words and corresponding coordinates
							_snake_case       =  [idx for idx, word in enumerate(_SCREAMING_SNAKE_CASE				) if not word.strip()]
							_snake_case       =  [word for idx, word in enumerate(_SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
							_snake_case       =  [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
							_snake_case       =  [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
							_snake_case       =  [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
							_snake_case       =  [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							_snake_case       =  []
							for x, y, w, h in zip(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
														_snake_case       =  [x, y, x + w, y + h]
														actual_boxes.append(_SCREAMING_SNAKE_CASE				)

							# finally, normalize the bounding boxes
							_snake_case       =  []
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)				)

							assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				), "Not as many words as there are bounding boxes"

							return words, normalized_boxes


class       _lowerCAmelCase   (   _a				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						["pixel_values"]

							def __init__(self  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  UpperCAmelCase = PILImageResampling.BILINEAR  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  UpperCAmelCase = ""  ,  **UpperCAmelCase  ,  )					->       List[str]:
														super().__init__(**_a  )
														_snake_case       =  size if size is not None else {"height": 224, "width": 224}
														_snake_case       =  get_size_dict(_a  )

														_snake_case       =  do_resize
														_snake_case       =  size
														_snake_case       =  resample
														_snake_case       =  apply_ocr
														_snake_case       =  ocr_lang
														_snake_case       =  tesseract_config

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = PILImageResampling.BILINEAR  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       Tuple:
														_snake_case       =  get_size_dict(_a  )
														if "height" not in size or "width" not in size:
																					raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
														_snake_case       =  (size["height"], size["width"])
														return resize(_a  ,  size=_a  ,  resample=_a  ,  data_format=_a  ,  **_a  )





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = ChannelDimension.FIRST  ,  **UpperCAmelCase  ,  )					->       str:
														_snake_case       =  do_resize if do_resize is not None else self.do_resize
														_snake_case       =  size if size is not None else self.size
														_snake_case       =  get_size_dict(_a  )
														_snake_case       =  resample if resample is not None else self.resample
														_snake_case       =  apply_ocr if apply_ocr is not None else self.apply_ocr
														_snake_case       =  ocr_lang if ocr_lang is not None else self.ocr_lang
														_snake_case       =  tesseract_config if tesseract_config is not None else self.tesseract_config

														_snake_case       =  make_list_of_images(_a  )

														if not valid_images(_a  ):
																					raise ValueError(
																					    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																					    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

														if do_resize and size is None:
																					raise ValueError("""Size must be specified if do_resize is True."""  )

														# All transformations expect numpy arrays.
														_snake_case       =  [to_numpy_array(_a  ) for image in images]

														if apply_ocr:
																					requires_backends(self  ,  """pytesseract"""  )
																					_snake_case       =  []
																					_snake_case       =  []
																					for image in images:
																												_snake_case       =  apply_tesseract(_a  ,  _a  ,  _a  )
																												words_batch.append(_a  )
																												boxes_batch.append(_a  )

														if do_resize:
																					_snake_case       =  [self.resize(image=_a  ,  size=_a  ,  resample=_a  ) for image in images]

														# flip color channels from RGB to BGR (as Detectron2 requires this)
														_snake_case       =  [flip_channel_order(_a  ) for image in images]
														_snake_case       =  [to_channel_dimension_format(_a  ,  _a  ) for image in images]

														_snake_case       =  BatchFeature(data={"""pixel_values""": images}  ,  tensor_type=_a  )

														if apply_ocr:
																					_snake_case       =  words_batch
																					_snake_case       =  boxes_batch
														return data

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


@require_torch_gpu
@slow
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1