


"""simple docstring"""







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

__lowercase    					=      logging.get_logger(__name__)


def      lowercase			(				A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  return [
      int(1_000 * (box[0] / width)       ),
      int(1_000 * (box[1] / height)       ),
      int(1_000 * (box[2] / width)       ),
      int(1_000 * (box[3] / height)       ),
  ]


def      lowercase			(				A_  ,   A_  ,   A_ = None       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		List[str]      							=			tesseract_config if tesseract_config is not None else ""

  # apply OCR
  a						:		Optional[Any]      							=			to_pil_image(A_       )
  a							,		a						:		List[str]      							=			pil_image.size
  a						:		List[str]      							=			pytesseract.image_to_data(A_  ,   lang=A_  ,   output_type="dict"  ,   config=A_       )
  a							,		a							,		a							,		a							,		a						:		List[str]      							=			data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

  return words, normalized_boxes







class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       ["""pixel_values"""]






    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BILINEAR				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Optional[str] = None				, __UpperCAmelCase				:    Optional[str] = ""				, **__UpperCAmelCase				:    int				, ):
      super().__init__(**__UpperCAmelCase)
      a						:		List[Any]      							=			size if size is not None else {"height": 224, "width": 224}
      a						:		Optional[int]      							=			get_size_dict(__UpperCAmelCase)

      a						:		List[Any]      							=			do_resize
      a						:		List[str]      							=			size
      a						:		List[str]      							=			resample
      a						:		str      							=			apply_ocr
      a						:		Tuple      							=			ocr_lang
      a						:		str      							=			tesseract_config






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Dict[str, int]				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BILINEAR				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    Dict				, ):
      a						:		Union[str, Any]      							=			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()}''')
      a						:		Tuple      							=			(size["height"], size["width"])
      return resize(__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    ImageInput				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = 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]				, ):
      a						:		str      							=			do_resize if do_resize is not None else self.do_resize
      a						:		int      							=			size if size is not None else self.size
      a						:		Tuple      							=			get_size_dict(__UpperCAmelCase)
      a						:		Tuple      							=			resample if resample is not None else self.resample
      a						:		Dict      							=			apply_ocr if apply_ocr is not None else self.apply_ocr
      a						:		Tuple      							=			ocr_lang if ocr_lang is not None else self.ocr_lang
      a						:		str      							=			tesseract_config if tesseract_config is not None else self.tesseract_config

      a						:		List[Any]      							=			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.")

      # All transformations expect numpy arrays.
      a						:		Optional[int]      							=			[to_numpy_array(__UpperCAmelCase) for image in images]

      if apply_ocr:
        requires_backends(self				, "pytesseract")
        a						:		Tuple      							=			[]
        a						:		Union[str, Any]      							=			[]
        for image in images:
          a							,		a						:		List[str]      							=			apply_tesseract(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
          words_batch.append(__UpperCAmelCase)
          boxes_batch.append(__UpperCAmelCase)

      if do_resize:
        a						:		Optional[int]      							=			[self.resize(image=__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      a						:		int      							=			[flip_channel_order(__UpperCAmelCase) for image in images]
      a						:		str      							=			[to_channel_dimension_format(__UpperCAmelCase				, __UpperCAmelCase) for image in images]

      a						:		Tuple      							=			BatchFeature(data={"pixel_values": images}				, tensor_type=__UpperCAmelCase)

      if apply_ocr:
        a						:		Any      							=			words_batch
        a						:		Dict      							=			boxes_batch
      return data







"""simple docstring"""






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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """simple docstring"""



            if self.rope_scaling is None:
                  return

            if not isinstance(self.rope_scaling		,       __lowerCAmelCase						) or len(self.rope_scaling						) != 2:
                  raise ValueError(
                      """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                      f'got {self.rope_scaling}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	self.rope_scaling.get("""factor"""		,       __lowerCAmelCase						)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                  raise ValueError(
                      f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
            if rope_scaling_factor is None or not isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) or rope_scaling_factor <= 1.0:
                  raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)



