






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_           = logging.get_logger(__name__)







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       Any   ):
				return [
				    int(10_00 * (box[0] / width)   ),
				    int(10_00 * (box[1] / height)   ),
				    int(10_00 * (box[2] / width)   ),
				    int(10_00 * (box[3] / height)   ),
				]







def      snake_case		(							UpperCAmelCase	:       np.ndarray,						UpperCAmelCase	:       Optional[str],						UpperCAmelCase	:       Optional[str]   ):
				A	 =      to_pil_image(UpperCAmelCase   )
				A ,		A	 =      pil_image.size
				A	 =      pytesseract.image_to_data(UpperCAmelCase,						lang=UpperCAmelCase,						output_type='dict',						config=UpperCAmelCase   )
				A ,		A ,		A ,		A ,		A	 =      data['text'], data['left'], data['top'], data['width'], data['height']

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

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

				# finally, normalize the bounding boxes
				A	 =      []
				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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["pixel_values"]
		def __init__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Dict[str, int] = None						,_SCREAMING_SNAKE_CASE		:       PILImageResampling = PILImageResampling.BILINEAR						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       float = 1 / 2_5_5						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = ""						,**_SCREAMING_SNAKE_CASE		:       List[str]						,)    ->   None:


						'''simple docstring'''
						super().__init__(**_SCREAMING_SNAKE_CASE		)
						A	 =      size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A	 =      get_size_dict(_SCREAMING_SNAKE_CASE		)

						A	 =      do_resize
						A	 =      size
						A	 =      resample
						A	 =      do_rescale
						A	 =      rescale_value
						A	 =      do_normalize
						A	 =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A	 =      image_std if image_std is not None else IMAGENET_STANDARD_STD
						A	 =      apply_ocr
						A	 =      ocr_lang
						A	 =      tesseract_config
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Dict[str, int]						,_SCREAMING_SNAKE_CASE		:       PILImageResampling = PILImageResampling.BILINEAR						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   np.ndarray:


						'''simple docstring'''
						A	 =      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()}'		)
						A	 =      (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       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Union[int, float]						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   np.ndarray:


						'''simple docstring'''
						return rescale(_SCREAMING_SNAKE_CASE						,scale=_SCREAMING_SNAKE_CASE						,data_format=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]]						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]]						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   np.ndarray:


						'''simple docstring'''
						return normalize(_SCREAMING_SNAKE_CASE						,mean=_SCREAMING_SNAKE_CASE						,std=_SCREAMING_SNAKE_CASE						,data_format=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)







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


						'''simple docstring'''
						A	 =      do_resize if do_resize is not None else self.do_resize
						A	 =      size if size is not None else self.size
						A	 =      get_size_dict(_SCREAMING_SNAKE_CASE		)
						A	 =      resample if resample is not None else self.resample
						A	 =      do_rescale if do_rescale is not None else self.do_rescale
						A	 =      rescale_factor if rescale_factor is not None else self.rescale_factor
						A	 =      do_normalize if do_normalize is not None else self.do_normalize
						A	 =      image_mean if image_mean is not None else self.image_mean
						A	 =      image_std if image_std is not None else self.image_std
						A	 =      apply_ocr if apply_ocr is not None else self.apply_ocr
						A	 =      ocr_lang if ocr_lang is not None else self.ocr_lang
						A	 =      tesseract_config if tesseract_config is not None else self.tesseract_config

						A	 =      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.
						A	 =      [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'		)
										A	 =      []
										A	 =      []
										for image in images:
														A ,		A	 =      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:
										A	 =      [self.resize(image=_SCREAMING_SNAKE_CASE						,size=_SCREAMING_SNAKE_CASE						,resample=_SCREAMING_SNAKE_CASE		) for image in images]

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

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

						A	 =      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) for image in images]

						A	 =      BatchFeature(data={'pixel_values': images}						,tensor_type=_SCREAMING_SNAKE_CASE		)

						if apply_ocr:
										A	 =      words_batch
										A	 =      boxes_batch
						return data













from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCAmelCase_           = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						requires_backends(self						,'vision'		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       int=None		)    ->   Any:


						'''simple docstring'''
						A	 =      {}
						A	 =      {}

						if prompt is not None:
										A	 =      prompt

						if generate_kwargs is not None:
										A	 =      generate_kwargs
						if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
														A	 =      {}
										if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
														raise ValueError(
														    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
														    ' please use only one'		)
										A	 =      max_new_tokens
						return preprocess_params, forward_kwargs, {}
		def __call__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, List[str], "Image.Image", List["Image.Image"]]						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return super().__call__(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None		)    ->   int:


						'''simple docstring'''
						A	 =      load_image(_SCREAMING_SNAKE_CASE		)

						if prompt is not None:
										if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														raise ValueError(
														    f'Received an invalid text input, got - {type(_SCREAMING_SNAKE_CASE		)} - but expected a single string. '
														    'Note also that one single text can be provided for conditional image to text generation.'		)

										A	 =      self.model.config.model_type

										if model_type == "git":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(text=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids
														A	 =      [self.tokenizer.cls_token_id] + input_ids
														A	 =      torch.tensor(_SCREAMING_SNAKE_CASE		).unsqueeze(0		)
														model_inputs.update({'input_ids': input_ids}		)

										elif model_type == "pix2struct":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,header_text=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

										elif model_type != "vision-encoder-decoder":
														# vision-encoder-decoder does not support conditional generation
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														model_inputs.update(_SCREAMING_SNAKE_CASE		)

										else:
														raise ValueError(f'Model type {model_type} does not support conditional text generation'		)

						else:
										A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
										A	 =      None

						return model_inputs
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Dict=None		)    ->   List[str]:


						'''simple docstring'''
						# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
						# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs['input_ids']						,_SCREAMING_SNAKE_CASE		)
						    and all(x is None for x in model_inputs['input_ids']		)
						):
										A	 =      None

						if generate_kwargs is None:
										A	 =      {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						A	 =      model_inputs.pop(self.model.main_input_name		)
						A	 =      self.model.generate(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						return model_outputs







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      []
						for output_ids in model_outputs:
										A	 =      {
										    'generated_text': self.tokenizer.decode(
										        _SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE						,)
										}
										records.append(_SCREAMING_SNAKE_CASE		)
						return records





