





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =tempfile.mkdtemp()

   lowerCamelCase__:				Any        =BlipImageProcessor()
   lowerCamelCase__:				Dict        =BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")

   lowerCamelCase__:				str        =BlipProcessor(UpperCAmelCase_	,			UpperCAmelCase_)

   processor.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			**UpperCAmelCase_    :       Union[str, Any])	->Any:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).tokenizer

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			**UpperCAmelCase_    :       Union[str, Any])	->Tuple:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).image_processor

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->str:



   '''simple docstring'''



   lowerCamelCase__:				str        =[np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta)]

   lowerCamelCase__:				Any        =[Image.fromarray(np.moveaxis(UpperCAmelCase_	,			0	,			-1)) for x in image_inputs]

   return image_inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =BlipProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor())
   processor.save_pretrained(self.tmpdirname)

   lowerCamelCase__:				List[Any]        =self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)")
   lowerCamelCase__:				Optional[int]        =self.get_image_processor(do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   lowerCamelCase__:				List[Any]        =BlipProcessor.from_pretrained(
       self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab())
   self.assertIsInstance(processor.tokenizer	,			UpperCAmelCase_)

   self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string())
   self.assertIsInstance(processor.image_processor	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_image_processor()
   lowerCamelCase__:				Optional[Any]        =self.get_tokenizer()

   lowerCamelCase__:				Optional[int]        =BlipProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				int        =self.prepare_image_inputs()

   lowerCamelCase__:				Optional[Any]        =image_processor(UpperCAmelCase_	,			return_tensors="np")
   lowerCamelCase__:				Tuple        =processor(images=UpperCAmelCase_	,			return_tensors="np")

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1E-2)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.get_image_processor()
   lowerCamelCase__:				Optional[Any]        =self.get_tokenizer()

   lowerCamelCase__:				Any        =BlipProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        ="lower newer"

   lowerCamelCase__:				List[Any]        =processor(text=UpperCAmelCase_)

   lowerCamelCase__:				Tuple        =tokenizer(UpperCAmelCase_	,			return_token_type_ids=UpperCAmelCase_)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,			encoded_processor[key])

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				str        =self.get_image_processor()
   lowerCamelCase__:				List[Any]        =self.get_tokenizer()

   lowerCamelCase__:				Dict        =BlipProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Any        ="lower newer"
   lowerCamelCase__:				Union[str, Any]        =self.prepare_image_inputs()

   lowerCamelCase__:				int        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

   self.assertListEqual(list(inputs.keys())	,			["pixel_values", "input_ids", "attention_mask"])

   # test if it raises when no input is passed
   with pytest.raises(UpperCAmelCase_):
     processor()

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.get_image_processor()
   lowerCamelCase__:				int        =self.get_tokenizer()

   lowerCamelCase__:				Union[str, Any]        =BlipProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Dict        =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   lowerCamelCase__:				List[Any]        =processor.batch_decode(UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =tokenizer.batch_decode(UpperCAmelCase_)

   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.get_image_processor()
   lowerCamelCase__:				Any        =self.get_tokenizer()

   lowerCamelCase__:				Optional[Any]        =BlipProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				int        ="lower newer"
   lowerCamelCase__:				Any        =self.prepare_image_inputs()

   lowerCamelCase__:				Optional[int]        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

   # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
   self.assertListEqual(list(inputs.keys())	,			["pixel_values", "input_ids", "attention_mask"])








import inspect
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_config_docstrings.py
__A     =    "src/transformers"


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

__A     =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A     =    re.compile(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


__A     =    {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =None

  # source code of `config_class`
  lowerCamelCase__:				List[Any]        =inspect.getsource(__a		)
  lowerCamelCase__:				str        =_re_checkpoint.findall(__a		)

  # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
  # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
  for ckpt_name, ckpt_link in checkpoints:
    # allow the link to end with `/`
    if ckpt_link.endswith("/"		):
      lowerCamelCase__:				str        =ckpt_link[:-1]

    # verify the checkpoint name corresponds to the checkpoint link
    lowerCamelCase__:				Optional[int]        =F"""https://huggingface.co/{ckpt_name}"""
    if ckpt_link == ckpt_link_from_name:
      lowerCamelCase__:				List[Any]        =ckpt_name
      break

  return checkpoint





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  for config_class in list(CONFIG_MAPPING.values()		):
    # Skip deprecated models
    if "models.deprecated" in config_class.__module__:
      continue
    lowerCamelCase__:				List[Any]        =get_checkpoint_from_config_class(__a		)

    lowerCamelCase__:				Tuple        =config_class.__name__
    if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
      configs_without_checkpoint.append(__a		)

  if len(__a		) > 0:
    lowerCamelCase__:				List[str]        ="\n".join(sorted(__a		)		)
    raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""		)


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()

