"""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

UpperCAmelCase       =  logging.get_logger(__name__)






def      lowercase		(						a__	:		List[str] ,  a__	:		Optional[Any] ,  a__	:		Dict     )						->   Any:
					return [
					    int(1000 * (box[0] / width)     ),
					    int(1000 * (box[1] / height)     ),
					    int(1000 * (box[2] / width)     ),
					    int(1000 * (box[3] / height)     ),
					]






def      lowercase		(						a__	:		List[str] ,  a__	:		Optional[Any] ,  a__	:		Dict = None     )						->   List[Any]:
					_UpperCamelCase      						=	tesseract_config if tesseract_config is not None else ''''''

					# apply OCR
					_UpperCamelCase      						=	to_pil_image(UpperCamelCase__     )
					_UpperCamelCase						,							_UpperCamelCase      						=	pil_image.size
					_UpperCamelCase      						=	pytesseract.image_to_data(UpperCamelCase__ ,  lang=UpperCamelCase__ ,  output_type='''dict''' ,  config=UpperCamelCase__     )
					_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase						,							_UpperCamelCase      						=	data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

					# finally, normalize the bounding boxes
					_UpperCamelCase      						=	[]
					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_       (						lowercase_):
							snake_case__        =      ['''pixel_values''']






							def __init__(		self :      Any     ,						__UpperCamelCase :      str = True     ,						__UpperCamelCase :      Any = None     ,						__UpperCamelCase :      Optional[int] = PILImageResampling.BILINEAR     ,						__UpperCamelCase :      Union[str, Any] = True     ,						__UpperCamelCase :      List[str] = None     ,						__UpperCamelCase :      int = ""     ,						**__UpperCamelCase :      Optional[Any]     ,						)       ->       Any:
												super().__init__(**__UpperCamelCase  )
												_UpperCamelCase      						=	size if size is not None else {'''height''': 224, '''width''': 224}
												_UpperCamelCase      						=	get_size_dict(__UpperCamelCase  )

												_UpperCamelCase      						=	do_resize
												_UpperCamelCase      						=	size
												_UpperCamelCase      						=	resample
												_UpperCamelCase      						=	apply_ocr
												_UpperCamelCase      						=	ocr_lang
												_UpperCamelCase      						=	tesseract_config






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      int = PILImageResampling.BILINEAR     ,						__UpperCamelCase :      int = None     ,						**__UpperCamelCase :      List[Any]     ,						)       ->       List[str]:
												_UpperCamelCase      						=	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()}'''  )
												_UpperCamelCase      						=	(size['''height'''], size['''width'''])
												return resize(__UpperCamelCase     ,						size=__UpperCamelCase     ,						resample=__UpperCamelCase     ,						data_format=__UpperCamelCase     ,						**__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      Union[str, Any] = None     ,						__UpperCamelCase :      Any = None     ,						__UpperCamelCase :      List[Any] = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      List[Any] = ChannelDimension.FIRST     ,						**__UpperCamelCase :      str     ,						)       ->       Any:
												_UpperCamelCase      						=	do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase      						=	size if size is not None else self.size
												_UpperCamelCase      						=	get_size_dict(__UpperCamelCase  )
												_UpperCamelCase      						=	resample if resample is not None else self.resample
												_UpperCamelCase      						=	apply_ocr if apply_ocr is not None else self.apply_ocr
												_UpperCamelCase      						=	ocr_lang if ocr_lang is not None else self.ocr_lang
												_UpperCamelCase      						=	tesseract_config if tesseract_config is not None else self.tesseract_config

												_UpperCamelCase      						=	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.
												_UpperCamelCase      						=	[to_numpy_array(__UpperCamelCase  ) for image in images]

												if apply_ocr:
																	requires_backends(self     ,						'''pytesseract'''  )
																	_UpperCamelCase      						=	[]
																	_UpperCamelCase      						=	[]
																	for image in images:
																						_UpperCamelCase						,							_UpperCamelCase      						=	apply_tesseract(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase  )
																						words_batch.append(__UpperCamelCase  )
																						boxes_batch.append(__UpperCamelCase  )

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

												# flip color channels from RGB to BGR (as Detectron2 requires this)
												_UpperCamelCase      						=	[flip_channel_order(__UpperCamelCase  ) for image in images]
												_UpperCamelCase      						=	[to_channel_dimension_format(__UpperCamelCase     ,						__UpperCamelCase  ) for image in images]

												_UpperCamelCase      						=	BatchFeature(data={'''pixel_values''': images}     ,						tensor_type=__UpperCamelCase  )

												if apply_ocr:
																	_UpperCamelCase      						=	words_batch
																	_UpperCamelCase      						=	boxes_batch
												return data



import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


_UpperCAmelCase		:   Dict										=       logging.get_logger(__name__)







class  lowercase				:




				def __init__(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   snake_case=None       ):
											if not conversation_id:
																		snake_case_							=    uuid.uuida()
											if past_user_inputs is None:
																		snake_case_							=    []
											if generated_responses is None:
																		snake_case_							=    []

											snake_case_							=    conversation_id
											snake_case_							=    past_user_inputs
											snake_case_							=    generated_responses
											snake_case_							=    text




				def __eq__(					self      ,   snake_case       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		return False
											if self.uuid == other.uuid:
																		return True
											return (
											    self.new_user_input == other.new_user_input
											    and self.past_user_inputs == other.past_user_inputs
											    and self.generated_responses == other.generated_responses
											)




				def     a							(					self      ,   snake_case      ,   snake_case = False       ):
											if self.new_user_input:
																		if overwrite:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
																									    F'''with: "{text}".'''       )
																									snake_case_							=    text
																		else:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
																									    F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''       )
											else:
																		snake_case_							=    text




				def     a							(					self       ):
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											snake_case_							=    None




				def     a							(					self      ,   snake_case       ):
											self.generated_responses.append(snake_case       )




				def     a							(					self       ):
											for user_input, generated_response in zip(self.past_user_inputs      ,   self.generated_responses       ):
																		yield True, user_input
																		yield False, generated_response
											if self.new_user_input:
																		yield True, self.new_user_input




				def __repr__(					self       ):
											snake_case_							=    F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		snake_case_							=    'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output







@add_end_docstrings(
    lowercase_	,  R'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''	,  )
class  lowercase				(       lowercase_			):




				def __init__(					self      ,   *snake_case      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )
											if self.tokenizer.pad_token_id is None:
																		snake_case_							=    self.tokenizer.eos_token




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    {}
											snake_case_							=    {}

											if min_length_for_response is not None:
																		snake_case_							=    min_length_for_response
											if minimum_tokens is not None:
																		snake_case_							=    minimum_tokens

											if "max_length" in generate_kwargs:
																		snake_case_							=    generate_kwargs['max_length']
																		# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
											if clean_up_tokenization_spaces is not None:
																		snake_case_							=    clean_up_tokenization_spaces

											if generate_kwargs:
																		forward_params.update(snake_case       )
											return preprocess_params, forward_params, postprocess_params




				def __call__(					self      ,   snake_case      ,   snake_case=0      ,   **snake_case       ):
											snake_case_							=    super().__call__(snake_case      ,   num_workers=snake_case      ,   **snake_case       )
											if isinstance(snake_case      ,   snake_case       ) and len(snake_case       ) == 1:
																		return outputs[0]
											return outputs




				def     a							(					self      ,   snake_case      ,   snake_case=32       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		raise ValueError('ConversationalPipeline, expects Conversation as inputs'       )
											if conversation.new_user_input is None:
																		raise ValueError(
																		    F'''Conversation with UUID {type(conversation.uuid       )} does not contain new user input to process. '''
																		    'Add user inputs with the conversation\'s `add_user_input` method'       )
											if hasattr(self.tokenizer      ,   '_build_conversation_input_ids'       ):
																		snake_case_							=    self.tokenizer._build_conversation_input_ids(snake_case       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		snake_case_							=    self._legacy_parse_and_tokenize(snake_case       )

											if self.framework == "pt":
																		snake_case_							=    torch.LongTensor([input_ids]       )
											elif self.framework == "tf":
																		snake_case_							=    tf.constant([input_ids]       )
											return {"input_ids": input_ids, "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=10      ,   **snake_case       ):
											snake_case_							=    generate_kwargs.get('max_length'      ,   self.model.config.max_length       )

											snake_case_							=    model_inputs['input_ids'].shape[1]
											if max_length - minimum_tokens < n:
																		logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''       )
																		snake_case_							=    max_length - minimum_tokens
																		snake_case_							=    model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									snake_case_							=    model_inputs['attention_mask'][:, -trim:]
											snake_case_							=    model_inputs.pop('conversation'       )
											snake_case_							=    max_length
											snake_case_							=    self.model.generate(**snake_case      ,   **snake_case       )
											if self.model.config.is_encoder_decoder:
																		snake_case_							=    1
											else:
																		snake_case_							=    n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=True       ):
											snake_case_							=    model_outputs['output_ids']
											snake_case_							=    self.tokenizer.decode(
											    output_ids[0]      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   )
											snake_case_							=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(snake_case       )
											return conversation




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.tokenizer.eos_token_id
											snake_case_							=    []
											for is_user, text in conversation.iter_texts():
																		if eos_token_id is not None:
																									input_ids.extend(self.tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       ) + [eos_token_id]       )
																		else:
																									input_ids.extend(self.tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )       )

											if len(snake_case       ) > self.tokenizer.model_max_length:
																		snake_case_							=    input_ids[-self.tokenizer.model_max_length :]
											return input_ids




