






"""simple docstring"""




import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Dict=3							,						UpperCAmelCase__		:						int=32							,						UpperCAmelCase__		:						Union[str, Any]=3							,						UpperCAmelCase__		:						Any=10							,						UpperCAmelCase__		:						Tuple=[10, 20, 30, 40]							,						UpperCAmelCase__		:						List[str]=[1, 1, 2, 1]							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						List[Any]="relu"							,						UpperCAmelCase__		:						List[Any]=3							,						UpperCAmelCase__		:						Dict=None							,						)					->       str:
   _a  :       Optional[Any]             =	parent
   _a  :       List[str]             =	batch_size
   _a  :       Optional[Any]             =	image_size
   _a  :       Dict             =	num_channels
   _a  :       List[str]             =	embeddings_size
   _a  :       str             =	hidden_sizes
   _a  :       Union[str, Any]             =	depths
   _a  :       Optional[Any]             =	is_training
   _a  :       Optional[Any]             =	use_labels
   _a  :       str             =	hidden_act
   _a  :       str             =	num_labels
   _a  :       List[Any]             =	scope
   _a  :       str             =	len(UpperCAmelCase__							)


 def _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   _a  :       Any             =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   _a  :       str             =	self.get_config()

   return config, pixel_values


 def _lowercase	(     self		:						Tuple							)					->       List[str]:
   return RegNetConfig(
       num_channels=self.num_channels							,						embeddings_size=self.embeddings_size							,						hidden_sizes=self.hidden_sizes							,						depths=self.depths							,						hidden_act=self.hidden_act							,						num_labels=self.num_labels							,						image_size=self.image_size							,						)


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						List[str]							)					->       Tuple:
   _a  :       Any             =	FlaxRegNetModel(config=UpperCAmelCase__							)
   _a  :       str             =	model(UpperCAmelCase__							)

   # Output shape (b, c, h, w)
   self.parent.assertEqual(
       result.last_hidden_state.shape							,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,						)


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Union[str, Any]             =	self.num_labels
   _a  :       Union[str, Any]             =	FlaxRegNetForImageClassification(config=UpperCAmelCase__							)
   _a  :       Optional[int]             =	model(UpperCAmelCase__							)
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)							)







 def _lowercase	(     self		:						Optional[int]							)					->       Any:
   _a  :       int             =	self.prepare_config_and_inputs()
   _a      ,  _a  :       Tuple             =	config_and_inputs
   _a  :       Optional[Any]             =	{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_flax
class        UpperCamelCase			(	snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       str			=					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCamelCase					:       Dict			=					False
 UpperCamelCase					:       Optional[Any]			=					False
 UpperCamelCase					:       int			=					False


 def _lowercase	(     self		:						List[str]							)					->       None:
   _a  :       str             =	FlaxRegNetModelTester(self							)
   _a  :       Optional[int]             =	ConfigTester(self							,						config_class=UpperCAmelCase__							,						has_text_modality=UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


 def _lowercase	(     self		:						Optional[int]							)					->       Union[str, Any]:
   return


 def _lowercase	(     self		:						Union[str, Any]							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							)					->       Any:
   _a  :       Tuple             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__							)


 @unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
 def _lowercase	(     self		:						Optional[Any]							)					->       Tuple:
   pass


 @unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[int]:
   pass


 def _lowercase	(     self		:						Tuple							)					->       Optional[Any]:
   _a      ,  _a  :       List[str]             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       Tuple             =	model_class(UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	inspect.signature(model.__call__							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _a  :       Optional[Any]             =	[*signature.parameters.keys()]

     _a  :       Tuple             =	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							)					->       Dict:
   def check_hidden_states_output(UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[int]							):
     _a  :       List[str]             =	model_class(UpperCAmelCase__							)

     _a  :       Any             =	model(**self._prepare_for_class(UpperCAmelCase__							,						UpperCAmelCase__							)							)

     _a  :       Tuple             =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _a  :       List[Any]             =	self.model_tester.num_stages
     self.assertEqual(len(UpperCAmelCase__							)							,						expected_num_stages + 1							)

   _a      ,  _a  :       Optional[int]             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       Dict             =	True
     check_hidden_states_output(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _a  :       Union[str, Any]             =	True

     check_hidden_states_output(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[Any]							)					->       Dict:
   _a      ,  _a  :       str             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__							):
       _a  :       Optional[Any]             =	self._prepare_for_class(UpperCAmelCase__							,						UpperCAmelCase__							)
       _a  :       Optional[int]             =	model_class(UpperCAmelCase__							)

       @jax.jit
       def model_jitted(UpperCAmelCase__		:						Union[str, Any]							,						**UpperCAmelCase__		:						Optional[Any]							):
         return model(pixel_values=UpperCAmelCase__							,						**UpperCAmelCase__							)

       with self.subTest("""JIT Enabled"""							):
         _a  :       str             =	model_jitted(**UpperCAmelCase__							).to_tuple()

       with self.subTest("""JIT Disabled"""							):
         with jax.disable_jit():
           _a  :       Any             =	model_jitted(**UpperCAmelCase__							).to_tuple()

       self.assertEqual(len(UpperCAmelCase__							)							,						len(UpperCAmelCase__							)							)
       for jitted_output, output in zip(UpperCAmelCase__							,						UpperCAmelCase__							):
         self.assertEqual(jitted_output.shape							,						output.shape							)







def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Dict             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
  return image






@require_flax
class        UpperCamelCase			(	unittest.TestCase      ):


 @cached_property
 def _lowercase	(     self		:						Any							)					->       List[str]:
   return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""							) if is_vision_available() else None







 @slow
 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Any             =	FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""							)

   _a  :       Optional[int]             =	self.default_image_processor
   _a  :       Dict             =	prepare_img()
   _a  :       List[Any]             =	image_processor(images=UpperCAmelCase__							,						return_tensors="""np"""							)

   _a  :       Optional[int]             =	model(**UpperCAmelCase__							)

   # verify the logits
   _a  :       Optional[int]             =	(1, 1000)
   self.assertEqual(outputs.logits.shape							,						UpperCAmelCase__							)

   _a  :       Optional[Any]             =	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

   self.assertTrue(jnp.allclose(outputs.logits[0, :3]							,						UpperCAmelCase__							,						atol=1E-4							)							)









"""simple docstring"""




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'spiece.model'}

_snake_case          =   {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						int=False							,						UpperCAmelCase__		:						Dict=True							,						UpperCAmelCase__		:						Union[str, Any]=False							,						UpperCAmelCase__		:						Dict="<s>"							,						UpperCAmelCase__		:						Any="</s>"							,						UpperCAmelCase__		:						Any="<unk>"							,						UpperCAmelCase__		:						int="<sep>"							,						UpperCAmelCase__		:						Tuple="<pad>"							,						UpperCAmelCase__		:						Any="<cls>"							,						UpperCAmelCase__		:						Optional[Any]="<mask>"							,						UpperCAmelCase__		:						int=["<eop>", "<eod>"]							,						UpperCAmelCase__		:						Optional[Dict[str, Any]] = None							,						**UpperCAmelCase__		:						List[str]							,						)					->       None:
   _a  :       Optional[int]             =	AddedToken(UpperCAmelCase__							,						lstrip=UpperCAmelCase__							,						rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							) else mask_token

   _a  :       Union[str, Any]             =	{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCAmelCase__							,						remove_space=UpperCAmelCase__							,						keep_accents=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						unk_token=UpperCAmelCase__							,						sep_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						cls_token=UpperCAmelCase__							,						mask_token=UpperCAmelCase__							,						additional_special_tokens=UpperCAmelCase__							,						sp_model_kwargs=self.sp_model_kwargs							,						**UpperCAmelCase__							,						)

   _a  :       Optional[Any]             =	3

   _a  :       Tuple             =	do_lower_case
   _a  :       Tuple             =	remove_space
   _a  :       Tuple             =	keep_accents
   _a  :       Tuple             =	vocab_file

   _a  :       Any             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(UpperCAmelCase__							)

   try:
     import jieba
   except ModuleNotFoundError as error:
     raise error.__class__(
         """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
         """See https://pypi.org/project/jieba/ for installation."""							)
   _a  :       int             =	jieba
   _a  :       Tuple             =	str.maketrans(""" \n"""							,						"""\u2582\u2583"""							)


 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def _lowercase	(     self		:						Optional[Any]							)					->       Any:
   return len(self.sp_model							)


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       int             =	{self.convert_ids_to_tokens(UpperCAmelCase__							): i for i in range(self.vocab_size							)}
   vocab.update(self.added_tokens_encoder							)
   return vocab


 def __getstate__(     self		:						Tuple							)					->       List[str]:
   _a  :       Tuple             =	self.__dict__.copy()
   _a  :       Tuple             =	None
   return state


 def __setstate__(     self		:						Any							,						UpperCAmelCase__		:						Dict							)					->       Dict:
   _a  :       Tuple             =	d

   # for backward compatibility
   if not hasattr(self							,						"""sp_model_kwargs"""							):
     _a  :       Tuple             =	{}

   _a  :       int             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(self.vocab_file							)


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						Union[str, Any]							)					->       Dict:
   if self.remove_space:
     _a  :       Optional[int]             =	""" """.join(inputs.strip().split()							)
   else:
     _a  :       List[Any]             =	inputs
   _a  :       int             =	outputs.replace("""``"""							,						"""\""""							).replace("""''"""							,						"""\""""							)

   if not self.keep_accents:
     _a  :       Optional[Any]             =	unicodedata.normalize("""NFKD"""							,						UpperCAmelCase__							)
     _a  :       Dict             =	"""""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__							)]							)
   if self.do_lower_case:
     _a  :       Union[str, Any]             =	outputs.lower()

   return outputs


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   _a  :       str             =	self.preprocess_text(UpperCAmelCase__							)
   _a  :       Dict             =	self.sp_model.encode(UpperCAmelCase__							,						out_type=UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	[]
   for piece in pieces:
     if len(UpperCAmelCase__							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
       _a  :       Dict             =	self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__							,						""""""							)							)
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
         if len(cur_pieces[0]							) == 1:
           _a  :       Dict             =	cur_pieces[1:]
         else:
           _a  :       Any             =	cur_pieces[0][1:]
       cur_pieces.append(piece[-1]							)
       new_pieces.extend(UpperCAmelCase__							)
     else:
       new_pieces.append(UpperCAmelCase__							)

   return new_pieces


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						int							)					->       int:
   return self.sp_model.PieceToId(UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Optional[Any]							)					->       Any:
   return self.sp_model.IdToPiece(UpperCAmelCase__							)


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Any							)					->       Dict:
   _a  :       Dict             =	"""""".join(UpperCAmelCase__							).replace(UpperCAmelCase__							,						""" """							).strip()
   return out_string


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Optional[Any]             =	[self.sep_token_id]
   _a  :       Dict             =	[self.cls_token_id]
   if token_ids_a is None:
     return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							,						UpperCAmelCase__		:						bool = False							)					->       List[int]:

   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCAmelCase__							,						token_ids_a=UpperCAmelCase__							,						already_has_special_tokens=UpperCAmelCase__							)

   if token_ids_a is not None:
     return ([0] * len(UpperCAmelCase__							)) + [1] + ([0] * len(UpperCAmelCase__							)) + [1, 1]
   return ([0] * len(UpperCAmelCase__							)) + [1, 1]


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Any             =	[self.sep_token_id]
   _a  :       Optional[Any]             =	[2]

   if token_ids_a is None:
     return len(token_ids_a + sep							) * [0] + cls_segment_id
   return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
   _a  :       Union[str, Any]             =	os.path.join(
       UpperCAmelCase__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

   if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase__							) and os.path.isfile(self.vocab_file							):
     copyfile(self.vocab_file							,						UpperCAmelCase__							)
   elif not os.path.isfile(self.vocab_file							):
     with open(UpperCAmelCase__							,						"""wb"""							) as fi:
       _a  :       Optional[Any]             =	self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase__							)

   return (out_vocab_file,)







 def _lowercase	(     self		:						Any							,						*UpperCAmelCase__		:						List[str]							,						**UpperCAmelCase__		:						List[str]							)					->       List[str]:
   _a  :       Tuple             =	super()._decode(*UpperCAmelCase__							,						**UpperCAmelCase__							)
   _a  :       Optional[Any]             =	text.replace(""" """							,						""""""							).replace("""\u2582"""							,						""" """							).replace("""\u2583"""							,						"""\n"""							)
   return text

