


'''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
UpperCamelCase__          = '''src/transformers'''
UpperCamelCase__          = '''docs/source/en'''
UpperCamelCase__          = '''.'''





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      str:
    with open(lowerCAmelCase__    ,   '''r'''    ,   encoding='''utf-8'''    ,   newline='''\n'''		) as f:
        UpperCAmelCase__     :						Optional[Any]						=							f.readlines()
    # Find the start prompt.
    UpperCAmelCase__     :						Union[str, Any]						=							0
    while not lines[start_index].startswith(lowerCAmelCase__		):
        start_index += 1
    start_index += 1

    UpperCAmelCase__     :						str						=							start_index
    while not lines[end_index].startswith(lowerCAmelCase__		):
        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 |
UpperCamelCase__          = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__          = re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
UpperCamelCase__          = 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.
UpperCamelCase__          = re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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





def    a__			(   lowerCAmelCase__		)       ->      Tuple:
    UpperCAmelCase__     :						Optional[int]						=							re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,   lowerCAmelCase__		)
    return [m.group(0		) for m in matches]





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[Any]:
    UpperCAmelCase__     :						int						=							2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase__		)
    UpperCAmelCase__     :						Optional[int]						=							(width - text_length) // 2
    UpperCAmelCase__     :						List[Any]						=							width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent





def    a__			(   )       ->      Dict:
    UpperCAmelCase__     :						Optional[Any]						=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    UpperCAmelCase__     :						int						=							{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    UpperCAmelCase__     :						Union[str, Any]						=							{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.
    UpperCAmelCase__     :						str						=							collections.defaultdict(lowerCAmelCase__		)
    UpperCAmelCase__     :						Tuple						=							collections.defaultdict(lowerCAmelCase__		)
    UpperCAmelCase__     :						Dict						=							collections.defaultdict(lowerCAmelCase__		)
    UpperCAmelCase__     :						Any						=							collections.defaultdict(lowerCAmelCase__		)
    UpperCAmelCase__     :						int						=							collections.defaultdict(lowerCAmelCase__		)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(lowerCAmelCase__		):
        UpperCAmelCase__     :						Optional[int]						=							None
        if attr_name.endswith('''Tokenizer'''		):
            UpperCAmelCase__     :						List[Any]						=							slow_tokenizers
            UpperCAmelCase__     :						Tuple						=							attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''		):
            UpperCAmelCase__     :						Dict						=							fast_tokenizers
            UpperCAmelCase__     :						Tuple						=							attr_name[:-13]
        elif _re_tf_models.match(lowerCAmelCase__		) is not None:
            UpperCAmelCase__     :						List[Any]						=							tf_models
            UpperCAmelCase__     :						Tuple						=							_re_tf_models.match(lowerCAmelCase__		).groups()[0]
        elif _re_flax_models.match(lowerCAmelCase__		) is not None:
            UpperCAmelCase__     :						Optional[Any]						=							flax_models
            UpperCAmelCase__     :						str						=							_re_flax_models.match(lowerCAmelCase__		).groups()[0]
        elif _re_pt_models.match(lowerCAmelCase__		) is not None:
            UpperCAmelCase__     :						int						=							pt_models
            UpperCAmelCase__     :						List[str]						=							_re_pt_models.match(lowerCAmelCase__		).groups()[0]

        if lookup_dict is not None:
            while len(lowerCAmelCase__		) > 0:
                if attr_name in model_name_to_prefix.values():
                    UpperCAmelCase__     :						Tuple						=							True
                    break
                # Try again after removing the last word in the name
                UpperCAmelCase__     :						List[Any]						=							''''''.join(camel_case_split(lowerCAmelCase__		)[:-1]		)

    # Let's build that table!
    UpperCAmelCase__     :						List[Any]						=							list(model_name_to_config.keys()		)
    model_names.sort(key=str.lower		)
    UpperCAmelCase__     :						str						=							['''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).
    UpperCAmelCase__     :						Tuple						=							[len(lowerCAmelCase__		) + 2 for c in columns]
    UpperCAmelCase__     :						Any						=							max([len(lowerCAmelCase__		) for name in model_names]		) + 2

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

    UpperCAmelCase__     :						Dict						=							{True: '''✅''', False: '''❌'''}
    for name in model_names:
        UpperCAmelCase__     :						Tuple						=							model_name_to_prefix[name]
        UpperCAmelCase__     :						int						=							[
            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(lowerCAmelCase__    ,   lowerCAmelCase__		) for l, w in zip(lowerCAmelCase__    ,   lowerCAmelCase__		)]		) + "|\n"
    return table





def    a__			(   lowerCAmelCase__=False		)       ->      Union[str, Any]:
    UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						List[Any]						=							_find_text_in_file(
        filename=os.path.join(lowerCAmelCase__    ,   '''index.md'''		)    ,   start_prompt='''<!--This table is updated automatically from the auto modules'''    ,   end_prompt='''<!-- End table-->'''    ,   )
    UpperCAmelCase__     :						int						=							get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(lowerCAmelCase__    ,   '''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__":
  UpperCamelCase__          = argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  UpperCamelCase__          = parser.parse_args()

  check_model_table(args.fix_and_overwrite)


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase__     :		Optional[Any]        = logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_				)
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   if self.framework == "tf":
     raise ValueError(f'The {self.__class__} is only available in PyTorch.')

   requires_backends(self       ,	"""vision""")
   self.check_model_type(SCREAMING_SNAKE_CASE_)


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   if "text_queries" in kwargs:
     lowercase__	: Any     		=   kwargs.pop("""text_queries""")

   if isinstance(SCREAMING_SNAKE_CASE_       ,	(str, Image.Image)):
     lowercase__	: Optional[Any]     		=   {"""image""": image, """candidate_labels""": candidate_labels}
   else:
     lowercase__	: int     		=   image
   lowercase__	: List[str]     		=   super().__call__(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return results


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {}
   if "threshold" in kwargs:
     lowercase__	: List[Any]     		=   kwargs["""threshold"""]
   if "top_k" in kwargs:
     lowercase__	: int     		=   kwargs["""top_k"""]
   return {}, {}, postprocess_params


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   load_image(inputs["""image"""])
   lowercase__	: Any     		=   inputs["""candidate_labels"""]
   if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     lowercase__	: List[str]     		=   candidate_labels.split(""",""")

   lowercase__	: Tuple     		=   torch.tensor([[image.height, image.width]]       ,	dtype=torch.intaa)
   for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Optional[Any]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     lowercase__	: Union[str, Any]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     yield {
         "is_last": i == len(SCREAMING_SNAKE_CASE_) - 1,
         "target_size": target_size,
         "candidate_label": candidate_label,
         **text_inputs,
         **image_features,
     }


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   model_inputs.pop("""target_size""")
   lowercase__	: Optional[int]     		=   model_inputs.pop("""candidate_label""")
   lowercase__	: Dict     		=   model_inputs.pop("""is_last""")

   lowercase__	: Union[str, Any]     		=   self.model(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
   return model_outputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   []
   for model_output in model_outputs:
     lowercase__	: Optional[int]     		=   model_output["""candidate_label"""]
     lowercase__	: Tuple     		=   BaseModelOutput(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   self.image_processor.post_process_object_detection(
         outputs=SCREAMING_SNAKE_CASE_       ,	threshold=SCREAMING_SNAKE_CASE_       ,	target_sizes=model_output["""target_size"""])[0]

     for index in outputs["scores"].nonzero():
       lowercase__	: Optional[Any]     		=   outputs["""scores"""][index].item()
       lowercase__	: Optional[Any]     		=   self._get_bounding_box(outputs["""boxes"""][index][0])

       lowercase__	: Tuple     		=   {"""score""": score, """label""": label, """box""": box}
       results.append(SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: x["score"]       ,	reverse=SCREAMING_SNAKE_CASE_)
   if top_k:
     lowercase__	: Any     		=   results[:top_k]

   return results



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.framework != "pt":
     raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
   lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   box.int().tolist()
   lowercase__	: Optional[int]     		=   {
       """xmin""": xmin,
       """ymin""": ymin,
       """xmax""": xmax,
       """ymax""": ymax,
   }
   return bbox






