




"""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      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[int]      ,       snake_case__   : int )     ->  Optional[int]:


				'''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					(snake_case__   : np.ndarray      ,       snake_case__   : Optional[str]      ,       snake_case__   : Optional[str] = None )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				tesseract_config if tesseract_config is not None else """"""

				# apply OCR
				lowerCAmelCase  =				to_pil_image(snake_case__ )
				lowerCAmelCase ,						lowerCAmelCase  =				pil_image.size
				lowerCAmelCase  =				pytesseract.image_to_data(snake_case__      ,       lang=snake_case__      ,       output_type="""dict"""      ,       config=snake_case__ )
				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(snake_case__ ) if not word.strip()]
				lowerCAmelCase  =				[word for idx, word in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]

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

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

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

				return words, normalized_boxes






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						['pixel_values']






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

										lowerCAmelCase  =				do_resize
										lowerCAmelCase  =				size
										lowerCAmelCase  =				resample
										lowerCAmelCase  =				apply_ocr
										lowerCAmelCase  =				ocr_lang
										lowerCAmelCase  =				tesseract_config






						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			np.ndarray						,						lowerCAmelCase				:			Dict[str, int]						,						lowerCAmelCase				:			PILImageResampling = PILImageResampling.BILINEAR						,						lowerCAmelCase				:			Optional[Union[str, ChannelDimension]] = None						,						**lowerCAmelCase				:			int						,						):
										lowerCAmelCase  =				get_size_dict(lowerCAmelCase					)
										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(lowerCAmelCase						,						size=lowerCAmelCase						,						resample=lowerCAmelCase						,						data_format=lowerCAmelCase						,						**lowerCAmelCase					)






						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			ImageInput						,						lowerCAmelCase				:			bool = None						,						lowerCAmelCase				:			Dict[str, int] = None						,						lowerCAmelCase				:			PILImageResampling = None						,						lowerCAmelCase				:			bool = None						,						lowerCAmelCase				:			Optional[str] = None						,						lowerCAmelCase				:			Optional[str] = None						,						lowerCAmelCase				:			Optional[Union[str, TensorType]] = None						,						lowerCAmelCase				:			ChannelDimension = ChannelDimension.FIRST						,						**lowerCAmelCase				:			Tuple						,						):
										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(lowerCAmelCase					)
										lowerCAmelCase  =				resample if resample is not None else self.resample
										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(lowerCAmelCase					)

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

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

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

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

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

										if apply_ocr:
														lowerCAmelCase  =				words_batch
														lowerCAmelCase  =				boxes_batch
										return data











"""simple docstring"""







import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a      =							'src/transformers'
a      =							'docs/source/en'
a      =							'.'





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple )     ->  Any:


				'''simple docstring'''






				with open(snake_case__      ,       """r"""      ,       encoding="""utf-8"""      ,       newline="""\n""" ) as f:
								lowerCAmelCase  =				f.readlines()
				# Find the start prompt.
				lowerCAmelCase  =				0
				while not lines[start_index].startswith(snake_case__ ):
								start_index += 1
				start_index += 1

				lowerCAmelCase  =				start_index
				while not lines[end_index].startswith(snake_case__ ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index] ) <= 1:
								start_index += 1
				while len(lines[end_index] ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
a      =							'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a      =							re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a      =							re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a      =							re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
a      =							direct_transformers_import(TRANSFORMERS_PATH)





def 							lowercase					(snake_case__   : int )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase  =				re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""      ,       snake_case__ )
				return [m.group(0 ) for m in matches]





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				2 if text == """✅""" or text == """❌""" else len(snake_case__ )
				lowerCAmelCase  =				(width - text_length) // 2
				lowerCAmelCase  =				width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase  =				{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase  =				{name: config.replace("""Config"""      ,       """""" ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(snake_case__ ):
								lowerCAmelCase  =				None
								if attr_name.endswith("""Tokenizer""" ):
												lowerCAmelCase  =				slow_tokenizers
												lowerCAmelCase  =				attr_name[:-9]
								elif attr_name.endswith("""TokenizerFast""" ):
												lowerCAmelCase  =				fast_tokenizers
												lowerCAmelCase  =				attr_name[:-13]
								elif _re_tf_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				tf_models
												lowerCAmelCase  =				_re_tf_models.match(snake_case__ ).groups()[0]
								elif _re_flax_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				flax_models
												lowerCAmelCase  =				_re_flax_models.match(snake_case__ ).groups()[0]
								elif _re_pt_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				pt_models
												lowerCAmelCase  =				_re_pt_models.match(snake_case__ ).groups()[0]

								if lookup_dict is not None:
												while len(snake_case__ ) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase  =				True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase  =				"""""".join(camel_case_split(snake_case__ )[:-1] )

    # Let's build that table!
				lowerCAmelCase  =				list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				lowerCAmelCase  =				["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase  =				[len(snake_case__ ) + 2 for c in columns]
				lowerCAmelCase  =				max([len(snake_case__ ) for name in model_names] ) + 2

				# Build the table per se
				lowerCAmelCase  =				"""|""" + """|""".join([_center_text(snake_case__      ,       snake_case__ ) for c, w in zip(snake_case__      ,       snake_case__ )] ) + """|\n"""
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"

				lowerCAmelCase  =				{True: """✅""", False: """❌"""}
				for name in model_names:
								lowerCAmelCase  =				model_name_to_prefix[name]
								lowerCAmelCase  =				[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(snake_case__      ,       snake_case__ ) for l, w in zip(snake_case__      ,       snake_case__ )] ) + "|\n"
				return table





def 							lowercase					(snake_case__   : Dict=False )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				_find_text_in_file(
				    filename=os.path.join(snake_case__      ,       """index.md""" )      ,       start_prompt="""<!--This table is updated automatically from the auto modules"""      ,       end_prompt="""<!-- End table-->"""      ,       )
				lowerCAmelCase  =				get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(snake_case__      ,       """index.md""" )      ,       """w"""      ,       encoding="""utf-8"""      ,       newline="""\n""" ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
								else:
												raise ValueError(
												    """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		a      =							parser.parse_args()

		check_model_table(args.fix_and_overwrite)





