import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )["last_hidden_state"]
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# compare the actual values for a slice of last dim
												self.assertTrue(jnp.allclose(output[:, :, -1]		,							_UpperCamelCase		,							atol=1e-3 ) )





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

_lowerCamelCase   :				str     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[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 		__lowerCamelCase							(UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							Optional[str] ,	UpperCAmelCase__       :							Optional[str]					):
					SCREAMING_SNAKE_CASE           =    to_pil_image(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    pil_image.size
					SCREAMING_SNAKE_CASE           =    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           =    data["text"], data["left"], data["top"], data["width"], data["height"]

					# filter empty words and corresponding coordinates
					SCREAMING_SNAKE_CASE           =    [idx for idx, word in enumerate(UpperCAmelCase__					) if not word.strip()]
					SCREAMING_SNAKE_CASE           =    [word for idx, word in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [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           =    []
					for x, y, w, h in zip(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    [x, y, x + w, y + h]
										actual_boxes.append(UpperCAmelCase__					)

					# finally, normalize the bounding boxes
					SCREAMING_SNAKE_CASE           =    []
					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       ):
							lowercase__  :    Optional[int]									=							["""pixel_values"""]




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   float = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = ""		,							**_UpperCamelCase				:   Union[str, Any]		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_value
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD
												SCREAMING_SNAKE_CASE           =    apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   List[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )
												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           =    (size["height"], size["width"])
												return resize(_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[int, float]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[float, Iterable[float]]		,							_UpperCamelCase				:   Union[float, Iterable[float]]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return normalize(_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   List[Any]		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std
												SCREAMING_SNAKE_CASE           =    apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												if not valid_images(_UpperCamelCase ):
																	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           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												# Tesseract OCR to get words + normalized bounding boxes
												if apply_ocr:
																	requires_backends(self		,							"pytesseract" )
																	SCREAMING_SNAKE_CASE           =    []
																	SCREAMING_SNAKE_CASE           =    []
																	for image in images:
																						SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    apply_tesseract(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
																						words_batch.append(_UpperCamelCase )
																						boxes_batch.append(_UpperCamelCase )

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

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    BatchFeature(data={"pixel_values": images}		,							tensor_type=_UpperCamelCase )

												if apply_ocr:
																	SCREAMING_SNAKE_CASE           =    words_batch
																	SCREAMING_SNAKE_CASE           =    boxes_batch
												return data




