


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class   __a      :
 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    ,			) ->    int:




        '''simple docstring'''
        lowercase__:       int        =       parent
        lowercase__:       List[Any]        =       batch_size
        lowercase__:       int        =       image_size
        lowercase__:       Union[str, Any]        =       num_channels
        lowercase__:       Dict        =       embeddings_size
        lowercase__:       List[Any]        =       hidden_sizes
        lowercase__:       List[Any]        =       depths
        lowercase__:       Optional[Any]        =       is_training
        lowercase__:       Optional[int]        =       use_labels
        lowercase__:       Optional[int]        =       hidden_act
        lowercase__:       List[str]        =       num_labels
        lowercase__:       Union[str, Any]        =       scope
        lowercase__:       str        =       len(_SCREAMING_SNAKE_CASE			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__:       Optional[int]        =       None
        if self.use_labels:
               lowercase__:       Dict        =       ids_tensor([self.batch_size]    ,			self.num_labels			)

        lowercase__:       Optional[Any]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return ResNetConfig(
            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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       str        =       TFResNetModel(config=_SCREAMING_SNAKE_CASE			)
        lowercase__:       List[str]        =       model(_SCREAMING_SNAKE_CASE			)
        # 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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.num_labels
        lowercase__:       Dict        =       TFResNetForImageClassification(_SCREAMING_SNAKE_CASE			)
        lowercase__:       Dict        =       model(_SCREAMING_SNAKE_CASE    ,			labels=_SCREAMING_SNAKE_CASE			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       config_and_inputs
        lowercase__:       List[str]        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   _UpperCamelCase	,	_UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[Any]       					=			(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
 __lowercase			:							List[str]       					=			(
     {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowercase			:							str       					=			False
 __lowercase			:							Union[str, Any]       					=			False
 __lowercase			:							str       					=			False
 __lowercase			:							Optional[int]       					=			False
 __lowercase			:							Any       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       TFResNetModelTester(self			)
        lowercase__:       List[Any]        =       ConfigTester(self    ,			config_class=_SCREAMING_SNAKE_CASE    ,			has_text_modality=_SCREAMING_SNAKE_CASE			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        return
 @unittest.skip(reason='ResNet does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        pass
 @unittest.skip(reason='ResNet does not support input and output embeddings'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       List[str]        =       self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__:       str        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			_SCREAMING_SNAKE_CASE			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       int        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        def check_hidden_states_output(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			):
               lowercase__:       Optional[Any]        =       model_class(_SCREAMING_SNAKE_CASE			)
               lowercase__:       Optional[Any]        =       model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE			)			)

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

               lowercase__:       Optional[Any]        =       self.model_tester.num_stages
               self.assertEqual(len(_SCREAMING_SNAKE_CASE			)    ,			expected_num_stages + 1			)

               # ResNet'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 // 4, self.model_tester.image_size // 4]    ,			)

        lowercase__      ,						lowercase__:       Tuple        =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__:       Union[str, Any]        =       ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
               for layer_type in layers_type:
                      lowercase__:       Tuple        =       layer_type
                      lowercase__:       Union[str, Any]        =       True
                      check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE			)

                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      lowercase__:       Optional[Any]        =       True

                      check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:       Union[str, Any]        =       TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)





def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       List[Any]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
            if is_vision_available()
            else None
        )
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       str        =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

        lowercase__:       Dict        =       self.default_image_processor
        lowercase__:       Dict        =       prepare_img()
        lowercase__:       int        =       image_processor(images=_SCREAMING_SNAKE_CASE    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       List[str]        =       model(**_SCREAMING_SNAKE_CASE			)

        # verify the logits
        lowercase__:       Optional[int]        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			_SCREAMING_SNAKE_CASE			)

        lowercase__:       str        =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

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






import os
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 logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''▁'''

__lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowerCAmelCase			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowerCAmelCase			=							{'''vinai/bartpho-syllable''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="<unk>"    ,			lowerCAmelCase__="<pad>"    ,			lowerCAmelCase__="<mask>"    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			unk_token=lowerCAmelCase__    ,			sep_token=lowerCAmelCase__    ,			cls_token=lowerCAmelCase__    ,			pad_token=lowerCAmelCase__    ,			mask_token=lowerCAmelCase__    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**lowerCAmelCase__    ,			)

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       int        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(lowerCAmelCase__			)			)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
               if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase__:       Optional[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False			) ->    List[int]:




        '''simple docstring'''

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

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase__			)) + [1]
        return [1] + ([0] * len(lowerCAmelCase__			)) + [1, 1] + ([0] * len(lowerCAmelCase__			)) + [1]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       Dict        =       [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]
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        else:
               return self.unk_token_id
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,			)

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

        if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
            lowerCAmelCase__			) and os.path.isfile(self.monolingual_vocab_file			):
               copyfile(self.monolingual_vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.monolingual_vocab_file			):
               with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as fp:
                      for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                    fp.write(F'{str(lowerCAmelCase__			)} \n'			)

        return out_vocab_file, out_monolingual_vocab_file


