"""simple docstring"""



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=3  ,						lowerCamelCase=32  ,						lowerCamelCase=3  ,						lowerCamelCase=10  ,						lowerCamelCase=[10, 20, 30, 40]  ,						lowerCamelCase=[1, 1, 2, 1]  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase="relu"  ,						lowerCamelCase=3  ,						lowerCamelCase=None  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  image_size
        __a     				=  num_channels
        __a     				=  embeddings_size
        __a     				=  hidden_sizes
        __a     				=  depths
        __a     				=  is_training
        __a     				=  use_labels
        __a     				=  hidden_act
        __a     				=  num_labels
        __a     				=  scope
        __a     				=  len(lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        __a     				=  None
        if self.use_labels:
               __a     				=  ids_tensor([self.batch_size]  ,						self.num_labels		)

        __a     				=  self.get_config()
        return config, pixel_values, labels


 def 		a__ (				self		):
        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  ,						)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  TFRegNetModel(config=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						training=lowerCamelCase		)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,						)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.num_labels
        __a     				=  TFRegNetForImageClassification(lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						labels=lowerCamelCase  ,						training=lowerCamelCase		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)






 def 		a__ (				self		):
        __a     				=  self.prepare_config_and_inputs()
        __a				, __a				, __a     				=  config_and_inputs
        __a     				=  {"pixel_values": pixel_values}
        return config, inputs_dict






@require_tf
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
 _snake_case   :			List[str]           =		(
     {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
     if is_tf_available()
     else {}
 )

 _snake_case   :			Tuple           =		False
 _snake_case   :			Dict           =		False
 _snake_case   :			str           =		False
 _snake_case   :			Optional[int]           =		False
 _snake_case   :			Tuple           =		False


 def 		a__ (				self		):
        __a     				=  TFRegNetModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase  ,						has_text_modality=lowerCamelCase		)


 def 		a__ (				self		):
        return


 @unittest.skip(reason="RegNet does not use inputs_embeds"		)
 def 		a__ (				self		):
        pass


 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"		)		) == 0  ,						reason="TF does not support backprop for grouped convolutions on CPU."  ,						)
 @slow
 def 		a__ (				self		):
        super().test_keras_fit()


 @unittest.skip(reason="RegNet does not support input and output embeddings"		)
 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)
               __a     				=  inspect.signature(model.call		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a     				=  [*signature.parameters.keys()]

               __a     				=  ["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)


 def 		a__ (				self		):
        def check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  model_class(lowerCamelCase		)
               __a     				=  model(**self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)  ,						training=lowerCamelCase		)

               __a     				=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __a     				=  self.model_tester.num_stages
               self.assertEqual(len(lowerCamelCase		)  ,						expected_num_stages + 1		)

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]		)  ,						[self.model_tester.image_size // 2, self.model_tester.image_size // 2]  ,						)

        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()
        __a     				=  ["basic", "bottleneck"]
        for model_class in self.all_model_classes:
               for layer_type in layers_type:
                      __a     				=  layer_type
                      __a     				=  True
                      check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      __a     				=  True

                      check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase={}		):
               __a     				=  model(lowerCamelCase  ,						return_dict=lowerCamelCase  ,						**lowerCamelCase		)
               __a     				=  model(lowerCamelCase  ,						return_dict=lowerCamelCase  ,						**lowerCamelCase		).to_tuple()

               def recursive_check(lowerCamelCase  ,						lowerCamelCase		):
                      if isinstance(lowerCamelCase  ,						(List, Tuple)		):
                             for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase  ,						lowerCamelCase		):
                                    recursive_check(lowerCamelCase  ,						lowerCamelCase		)
                      elif tuple_object is None:
                             return
                      else:
                             self.assertTrue(
                                 all(tf.equal(lowerCamelCase  ,						lowerCamelCase		)		)  ,						msg=(
                                     "Tuple and dict output are not equal. Difference:"
                                     F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object		)		)}"
                                 )  ,						)

                      recursive_check(lowerCamelCase  ,						lowerCamelCase		)

        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)

               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)
               check_equivalence(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase  ,						return_labels=lowerCamelCase		)
               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase  ,						return_labels=lowerCamelCase		)
               check_equivalence(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)
               check_equivalence(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						{"output_hidden_states": True}		)

               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase  ,						return_labels=lowerCamelCase		)
               __a     				=  self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase  ,						return_labels=lowerCamelCase		)
               check_equivalence(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						{"output_hidden_states": True}		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  TFRegNetModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)



def 					_lowerCamelCase(					):
       __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       return image






@require_tf
@require_vision
class 				snake_case__     (   unittest.TestCase   ):


 @cached_property
 def 		a__ (				self		):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
            if is_vision_available()
            else None
        )






 @slow
 def 		a__ (				self		):
        __a     				=  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

        __a     				=  self.default_image_processor
        __a     				=  prepare_img()
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="tf"		)

        # forward pass
        __a     				=  model(**lowerCamelCase  ,						training=lowerCamelCase		)

        # verify the logits
        __a     				=  tf.TensorShape((1, 1000)		)
        self.assertEqual(outputs.logits.shape  ,						lowerCamelCase		)

        __a     				=  tf.constant([-0.4180, -1.5051, -3.4836]		)

        tf.debugging.assert_near(outputs.logits[0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

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






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
