
from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

_lowercase											=    logging.get_logger(__name__)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str				)			->						str:
       return [
           int(1_0_0_0 * (box[0] / width)				),
           int(1_0_0_0 * (box[1] / height)				),
           int(1_0_0_0 * (box[2] / width)				),
           int(1_0_0_0 * (box[3] / height)				),
       ]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     np.ndarray			,							UpperCAmelCase_	:     Optional[str]			,							UpperCAmelCase_	:     Optional[str]				)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				str		=to_pil_image(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				int		=pil_image.size
       SCREAMING_SNAKE_CASE_							:				str		=pytesseract.image_to_data(UpperCAmelCase_			,							lang=UpperCAmelCase_			,							output_type='''dict'''			,							config=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

       # filter empty words and corresponding coordinates
       SCREAMING_SNAKE_CASE_							:				Tuple		=[idx for idx, word in enumerate(UpperCAmelCase_				) if not word.strip()]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=[word for idx, word in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
       SCREAMING_SNAKE_CASE_							:				List[Any]		=[coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
       SCREAMING_SNAKE_CASE_							:				List[Any]		=[coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       SCREAMING_SNAKE_CASE_							:				Dict		=[]
       for x, y, w, h in zip(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				):
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[x, y, x + w, y + h]
              actual_boxes.append(UpperCAmelCase_				)

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

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

       return words, normalized_boxes


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						["pixel_values"]


       def __init__(    self						,       __A = True						,       __A = None						,       __A = PILImageResampling.BILINEAR						,       __A = True						,       __A = 1 / 255						,       __A = True						,       __A = None						,       __A = None						,       __A = True						,       __A = None						,       __A = ""						,       **__A						,       )						->    None:
              super().__init__(**__A				)
              SCREAMING_SNAKE_CASE_							:				int		=size if size is not None else {'''height''': 224, '''width''': 224}
              SCREAMING_SNAKE_CASE_							:				Any		=get_size_dict(__A				)

              SCREAMING_SNAKE_CASE_							:				Any		=do_resize
              SCREAMING_SNAKE_CASE_							:				Dict		=size
              SCREAMING_SNAKE_CASE_							:				Any		=resample
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=rescale_value
              SCREAMING_SNAKE_CASE_							:				Any		=do_normalize
              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              SCREAMING_SNAKE_CASE_							:				Tuple		=image_std if image_std is not None else IMAGENET_STANDARD_STD
              SCREAMING_SNAKE_CASE_							:				List[str]		=apply_ocr
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=ocr_lang
              SCREAMING_SNAKE_CASE_							:				Dict		=tesseract_config


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = PILImageResampling.BILINEAR						,       __A = None						,       **__A						,       )						->    np.ndarray:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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()}'				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=(size['''height'''], size['''width'''])
              return resize(__A						,       size=__A						,       resample=__A						,       data_format=__A						,       **__A				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = None						,       **__A						,       )						->    np.ndarray:
              return rescale(__A						,       scale=__A						,       data_format=__A						,       **__A				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A = None						,       **__A						,       )						->    np.ndarray:
              return normalize(__A						,       mean=__A						,       std=__A						,       data_format=__A						,       **__A				)





       def 	_snake_case				(    self						,       __A						,       __A = None						,       __A = None						,       __A=None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = ChannelDimension.FIRST						,       **__A						,       )						->    PIL.Image.Image:
              SCREAMING_SNAKE_CASE_							:				Any		=do_resize if do_resize is not None else self.do_resize
              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else self.size
              SCREAMING_SNAKE_CASE_							:				Dict		=get_size_dict(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=resample if resample is not None else self.resample
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=do_rescale if do_rescale is not None else self.do_rescale
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=rescale_factor if rescale_factor is not None else self.rescale_factor
              SCREAMING_SNAKE_CASE_							:				int		=do_normalize if do_normalize is not None else self.do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_mean if image_mean is not None else self.image_mean
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_std if image_std is not None else self.image_std
              SCREAMING_SNAKE_CASE_							:				List[str]		=apply_ocr if apply_ocr is not None else self.apply_ocr
              SCREAMING_SNAKE_CASE_							:				Dict		=ocr_lang if ocr_lang is not None else self.ocr_lang
              SCREAMING_SNAKE_CASE_							:				str		=tesseract_config if tesseract_config is not None else self.tesseract_config

              SCREAMING_SNAKE_CASE_							:				Any		=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.'''				)

              if do_rescale and rescale_factor is None:
                     raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''				)

              # All transformations expect numpy arrays.
              SCREAMING_SNAKE_CASE_							:				Tuple		=[to_numpy_array(__A				) for image in images]

              # Tesseract OCR to get words + normalized bounding boxes
              if apply_ocr:
                     requires_backends(self						,       '''pytesseract'''				)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]
                     for image in images:
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=apply_tesseract(__A						,       __A						,       __A				)
                            words_batch.append(__A				)
                            boxes_batch.append(__A				)

              if do_resize:
                     SCREAMING_SNAKE_CASE_							:				str		=[self.resize(image=__A						,       size=__A						,       resample=__A				) for image in images]

              if do_rescale:
                     SCREAMING_SNAKE_CASE_							:				int		=[self.rescale(image=__A						,       scale=__A				) for image in images]

              if do_normalize:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[self.normalize(image=__A						,       mean=__A						,       std=__A				) for image in images]

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[to_channel_dimension_format(__A						,       __A				) for image in images]

              SCREAMING_SNAKE_CASE_							:				Dict		=BatchFeature(data={'''pixel_values''': images}						,       tensor_type=__A				)

              if apply_ocr:
                     SCREAMING_SNAKE_CASE_							:				str		=words_batch
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=boxes_batch
              return data


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

              if hasattr(__A						,       '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
