
"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   MvpTokenizer
				__UpperCamelCase      :						Any					       =   MvpTokenizerFast
				__UpperCamelCase      :						Optional[int]					       =   True
				__UpperCamelCase      :						int					       =   filter_roberta_detectors



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											super().setUp()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   dict(zip(SCREAMING_SNAKE_CASE__   ,       range(len(SCREAMING_SNAKE_CASE__    )    )    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__						:       str           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""merges_file"""]    )
											with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		fp.write(json.dumps(SCREAMING_SNAKE_CASE__    ) + """\n"""    )
											with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		fp.write("""\n""".join(SCREAMING_SNAKE_CASE__    )    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return "lower newer", "lower newer"



				@cached_property
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )



				@cached_property
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )



				@require_torch
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       max_length=len(SCREAMING_SNAKE_CASE__    )   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

																		self.assertEqual((2, 9)   ,       batch.input_ids.shape    )
																		self.assertEqual((2, 9)   ,       batch.attention_mask.shape    )
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   batch.input_ids.tolist()[0]
																		self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		# Test that special tokens are reset



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		# check if input_ids are returned and no labels
																		self.assertIn("""input_ids"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertIn("""attention_mask"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertNotIn("""labels"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertNotIn("""decoder_attention_mask"""   ,       SCREAMING_SNAKE_CASE__    )



				@require_torch
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   [
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer(text_target=SCREAMING_SNAKE_CASE__   ,       max_length=32   ,       padding="""max_length"""   ,       return_tensors="""pt"""    )
																		self.assertEqual(32   ,       targets["""input_ids"""].shape[1]    )



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer(
																		    ["""I am a small frog""" * 10_24, """I am a small frog"""]   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(batch.input_ids.shape   ,       (2, 10_24)    )



				@require_torch
				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ["""A long paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       text_target=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   inputs["""input_ids"""]
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   inputs["""labels"""]
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """A, <mask> AllenNLP sentence."""
																									SCREAMING_SNAKE_CASE__						:       int           =   tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r["""token_type_ids"""]    )   ,       sum(tokens_p["""token_type_ids"""]    )    )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )   ,       sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )   ,       )

																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p["""input_ids"""]   ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
																									self.assertSequenceEqual(tokens_r["""input_ids"""]   ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

																									self.assertSequenceEqual(
																									    SCREAMING_SNAKE_CASE__   ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																									self.assertSequenceEqual(
																									    SCREAMING_SNAKE_CASE__   ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )











'''simple docstring'''
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__							:							List[Any] =logging.get_logger(__name__)

a__							:							List[Any] ={
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_text_model"






 def __init__(       self       :							str							,	__A       :							List[Any]=2_5_0_0_0_2							,	__A       :							Any=1_0_2_4							,	__A       :							int=2_4							,	__A       :							Dict=1_6							,	__A       :							Optional[Any]=4_0_9_6							,	__A       :							Union[str, Any]="gelu"							,	__A       :							Dict=0.1							,	__A       :							Dict=0.1							,	__A       :							List[str]=5_1_4							,	__A       :							Optional[int]=1							,	__A       :							int=0.02							,	__A       :							Optional[Any]=0.02							,	__A       :							Optional[Any]=1e-05							,	__A       :							Dict=1							,	__A       :							List[Any]=0							,	__A       :							int=2							,	__A       :							Tuple="absolute"							,	__A       :							Optional[Any]=True							,	__A       :							Optional[int]=7_6_8							,	**__A       :							List[str]							,	):
        super().__init__(pad_token_id=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	**__A	)

        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  type_vocab_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  position_embedding_type
        __UpperCamelCase       =  use_cache
        __UpperCamelCase       =  project_dim






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_vision_model"






 def __init__(       self       :							List[Any]							,	__A       :							Union[str, Any]=7_6_8							,	__A       :							Optional[int]=3_0_7_2							,	__A       :							Optional[Any]=5_1_2							,	__A       :							Tuple=1_2							,	__A       :							Union[str, Any]=1_2							,	__A       :							Optional[int]=3							,	__A       :							Dict=2_2_4							,	__A       :							Tuple=3_2							,	__A       :							str="quick_gelu"							,	__A       :							Dict=1e-5							,	__A       :							Optional[int]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							int=1.0							,	**__A       :							Optional[int]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  hidden_act






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[Any]							,	__A       :							Union[str, os.PathLike]							,	**__A       :							Optional[Any]	):
        cls._set_token_in_kwargs(__A	)

        __UpperCamelCase       ,						__UpperCamelCase       =  cls.get_config_dict(__A							,	**__A	)

        # get the vision config dict if we are loading from AltCLIPConfig
        if config_dict.get('model_type'	) == "altclip":
               __UpperCamelCase       =  config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls							,	'model_type'	) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

        return cls.from_dict(__A							,	**__A	)






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					="altclip"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=True






 def __init__(       self       :							Any							,	__A       :							List[str]=None							,	__A       :							List[Any]=None							,	__A       :							List[str]=7_6_8							,	__A       :							List[str]=2.6592							,	**__A       :							Dict	):
        # If `_config_dict` exist, we use them for the backward compatibility.
        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
        # of confusion!).
        __UpperCamelCase       =  kwargs.pop('text_config_dict'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('vision_config_dict'							,	__A	)

        super().__init__(**__A	)

        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
        if text_config_dict is not None:
               if text_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `text_config_dict`.
               __UpperCamelCase       =  AltCLIPTextConfig(**__A	).to_dict()

               # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
               for key, value in _text_config_dict.items():
                      if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                             # If specified in `text_config_dict`
                             if key in text_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                                        f'''The value `text_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                                        f'''value `text_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
               text_config.update(_text_config_dict	)

        if vision_config_dict is not None:
               if vision_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `vision_config_dict`.
               __UpperCamelCase       =  AltCLIPVisionConfig(**__A	).to_dict()
               # convert keys to string instead of integer
               if "id2label" in _vision_config_dict:
                      __UpperCamelCase       =  {
                          str(__A	): value for key, value in _vision_config_dict['id2label'].items()
                      }

               # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
               for key, value in _vision_config_dict.items():
                      if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                             # If specified in `vision_config_dict`
                             if key in vision_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                                        f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                                        f'''The value `vision_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
               vision_config.update(_vision_config_dict	)

        if text_config is None:
               __UpperCamelCase       =  {}
               logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'	)

        if vision_config is None:
               __UpperCamelCase       =  {}
               logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'	)

        __UpperCamelCase       =  AltCLIPTextConfig(**__A	)
        __UpperCamelCase       =  AltCLIPVisionConfig(**__A	)

        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  logit_scale_init_value
        __UpperCamelCase       =  1.0






 @classmethod
 def        _lowerCamelCase					(       cls       :							Union[str, Any]							,	__A       :							AltCLIPTextConfig							,	__A       :							AltCLIPVisionConfig							,	**__A       :							Optional[Any]	):
        return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)
        __UpperCamelCase       =  self.text_config.to_dict()
        __UpperCamelCase       =  self.vision_config.to_dict()
        __UpperCamelCase       =  self.__class__.model_type
        return output
