"""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

_a      :		int           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Any	)       ->      Union[str, Any]:
  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     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       Optional[str]			,_lowerCamelCase						:       Optional[str] = None	)       ->      Optional[Any]:
  _lowerCAmelCase						:       Dict  			=     tesseract_config if tesseract_config is not None else """"""

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

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

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

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

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

  return words, normalized_boxes






class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		["pixel_values"]






     def __init__(		self				,     a__ = True				,     a__ = None				,     a__ = PILImageResampling.BILINEAR				,     a__ = True				,     a__ = None				,     a__ = ""				,     **a__				,     ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       Tuple  			=     size if size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase						:       Dict  			=     get_size_dict(a__  )

       _lowerCAmelCase						:       int  			=     do_resize
       _lowerCAmelCase						:       Optional[int]  			=     size
       _lowerCAmelCase						:       Optional[Any]  			=     resample
       _lowerCAmelCase						:       List[Any]  			=     apply_ocr
       _lowerCAmelCase						:       Dict  			=     ocr_lang
       _lowerCAmelCase						:       Tuple  			=     tesseract_config






     def     __A  (		self				,     a__				,     a__				,     a__ = PILImageResampling.BILINEAR				,     a__ = None				,     **a__				,     ):
       _lowerCAmelCase						:       Dict  			=     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()}"  )
       _lowerCAmelCase						:       Optional[int]  			=     (size["""height"""], size["""width"""])
       return resize(a__				,     size=a__				,     resample=a__				,     data_format=a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = ChannelDimension.FIRST				,     **a__				,     ):
       _lowerCAmelCase						:       str  			=     do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:       str  			=     size if size is not None else self.size
       _lowerCAmelCase						:       Optional[Any]  			=     get_size_dict(a__  )
       _lowerCAmelCase						:       List[Any]  			=     resample if resample is not None else self.resample
       _lowerCAmelCase						:       Any  			=     apply_ocr if apply_ocr is not None else self.apply_ocr
       _lowerCAmelCase						:       Dict  			=     ocr_lang if ocr_lang is not None else self.ocr_lang
       _lowerCAmelCase						:       int  			=     tesseract_config if tesseract_config is not None else self.tesseract_config

       _lowerCAmelCase						:       str  			=     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.
       _lowerCAmelCase						:       Optional[int]  			=     [to_numpy_array(a__  ) for image in images]

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

       if do_resize:
         _lowerCAmelCase						:       List[Any]  			=     [self.resize(image=a__				,     size=a__				,     resample=a__  ) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       _lowerCAmelCase						:       Union[str, Any]  			=     [flip_channel_order(a__  ) for image in images]
       _lowerCAmelCase						:       Tuple  			=     [to_channel_dimension_format(a__				,     a__  ) for image in images]

       _lowerCAmelCase						:       Any  			=     BatchFeature(data={"""pixel_values""": images}				,     tensor_type=a__  )

       if apply_ocr:
         _lowerCAmelCase						:       Optional[Any]  			=     words_batch
         _lowerCAmelCase						:       Optional[int]  			=     boxes_batch
       return data












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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''llama'''
      __snake_case       =  ['''past_key_values''']







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Union[str, Any]=32_000 ,					__UpperCAmelCase      :						str=4_096 ,					__UpperCAmelCase      :						int=11_008 ,					__UpperCAmelCase      :						Tuple=32 ,					__UpperCAmelCase      :						Optional[int]=32 ,					__UpperCAmelCase      :						List[str]=None ,					__UpperCAmelCase      :						Union[str, Any]="silu" ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						Optional[Any]=0.02 ,					__UpperCAmelCase      :						Any=1e-6 ,					__UpperCAmelCase      :						Union[str, Any]=True ,					__UpperCAmelCase      :						Optional[int]=0 ,					__UpperCAmelCase      :						Optional[int]=1 ,					__UpperCAmelCase      :						Optional[int]=2 ,					__UpperCAmelCase      :						Tuple=1 ,					__UpperCAmelCase      :						List[str]=False ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Tuple ,					)    ->str:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      max_position_embeddings
       a		  =      hidden_size
       a		  =      intermediate_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads

       # for backward compatibility
       if num_key_value_heads is None:
        a		  =      num_attention_heads

       a		  =      num_key_value_heads
       a		  =      hidden_act
       a		  =      initializer_range
       a		  =      rms_norm_eps
       a		  =      pretraining_tp
       a		  =      use_cache
       a		  =      rope_scaling
       self._rope_scaling_validation()

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







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Tuple:




       """simple docstring"""


       if self.rope_scaling is None:
        return

       if not isinstance(self.rope_scaling ,					__UpperCAmelCase  ) 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}"""  )
       a		  =      self.rope_scaling.get('''type''' ,					__UpperCAmelCase  )
       a		  =      self.rope_scaling.get('''factor''' ,					__UpperCAmelCase  )
       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(__UpperCAmelCase ,					__UpperCAmelCase  ) or rope_scaling_factor <= 1.0:
        raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""  )


