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__			:				Any		=	logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__							)     ->	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    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__							)     ->	Optional[Any]:
							lowerCAmelCase			=		to_pil_image(__A							)
							lowerCAmelCase		,      lowerCAmelCase			=		pil_image.size
							lowerCAmelCase			=		pytesseract.image_to_data(__A   ,  lang=__A   ,  output_type='''dict'''   ,  config=__A							)
							lowerCAmelCase		,      lowerCAmelCase		,      lowerCAmelCase		,      lowerCAmelCase		,      lowerCAmelCase			=		data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

							# finally, normalize the bounding boxes
							lowerCAmelCase			=		[]
							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    lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  :					Tuple   =				['pixel_values']


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

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


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR		,			__SCREAMING_SNAKE_CASE = None		,			**__SCREAMING_SNAKE_CASE		,			)						->np.ndarray:
								lowerCAmelCase			=		get_size_dict(__SCREAMING_SNAKE_CASE				)
								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			=		(size['''height'''], size['''width'''])
								return resize(__SCREAMING_SNAKE_CASE		,			size=__SCREAMING_SNAKE_CASE		,			resample=__SCREAMING_SNAKE_CASE		,			data_format=__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)


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


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




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST		,			**__SCREAMING_SNAKE_CASE		,			)						->PIL.Image.Image:
								lowerCAmelCase			=		do_resize if do_resize is not None else self.do_resize
								lowerCAmelCase			=		size if size is not None else self.size
								lowerCAmelCase			=		get_size_dict(__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		resample if resample is not None else self.resample
								lowerCAmelCase			=		do_rescale if do_rescale is not None else self.do_rescale
								lowerCAmelCase			=		rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCAmelCase			=		do_normalize if do_normalize is not None else self.do_normalize
								lowerCAmelCase			=		image_mean if image_mean is not None else self.image_mean
								lowerCAmelCase			=		image_std if image_std is not None else self.image_std
								lowerCAmelCase			=		apply_ocr if apply_ocr is not None else self.apply_ocr
								lowerCAmelCase			=		ocr_lang if ocr_lang is not None else self.ocr_lang
								lowerCAmelCase			=		tesseract_config if tesseract_config is not None else self.tesseract_config

								lowerCAmelCase			=		make_list_of_images(__SCREAMING_SNAKE_CASE				)

								if not valid_images(__SCREAMING_SNAKE_CASE				):
															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.
								lowerCAmelCase			=		[to_numpy_array(__SCREAMING_SNAKE_CASE				) for image in images]

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

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

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

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

								lowerCAmelCase			=		[to_channel_dimension_format(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				) for image in images]

								lowerCAmelCase			=		BatchFeature(data={'''pixel_values''': images}		,			tensor_type=__SCREAMING_SNAKE_CASE				)

								if apply_ocr:
															lowerCAmelCase			=		words_batch
															lowerCAmelCase			=		boxes_batch
								return data








'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



