





'''simple docstring'''







import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 				__SCREAMING_SNAKE_CASE  (__lowerCamelCase       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =GPTaTokenizer
				__a            =GPTaTokenizerFast
				__a            =True
				__a            ={"add_prefix_space": True}
				__a            =False




				def   UpperCamelCase__   (  self     :					Any					):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								_a								= [
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "\u0120",
								    "\u0120l",
								    "\u0120n",
								    "\u0120lo",
								    "\u0120low",
								    "er",
								    "\u0120lowest",
								    "\u0120newer",
								    "\u0120wider",
								    "<unk>",
								    "<|endoftext|>",
								]
								_a								= dict(zip(__A					,				range(len(__A					)					)					)					)
								_a								= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
								_a								= {"unk_token": "<unk>"}

								_a								= os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES["vocab_file"]					)
								_a								= 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(__A					) + "\n"					)
								with open(self.merges_file					,				"w"					,				encoding="utf-8"					) as fp:
												fp.write("\n".join(__A					)					)




				def   UpperCamelCase__   (  self     :					Tuple					,				**__a     :					Any					):
								kwargs.update(self.special_tokens_map					)
								return GPTaTokenizer.from_pretrained(self.tmpdirname					,				**__A					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				**__a     :					List[str]					):
								kwargs.update(self.special_tokens_map					)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,				**__A					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					str					):
								_a								= "lower newer"
								_a								= "lower newer"
								return input_text, output_text




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= GPTaTokenizer(self.vocab_file					,				self.merges_file					,				**self.special_tokens_map					)
								_a								= "lower newer"
								_a								= ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
								_a								= tokenizer.tokenize(__A					,				add_prefix_space=__A					)
								self.assertListEqual(__A					,				__A					)

								_a								= tokens + [tokenizer.unk_token]
								_a								= [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__A					)					,				__A					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								if not self.test_rust_tokenizer:
												return

								_a								= self.get_tokenizer()
								_a								= self.get_rust_tokenizer(add_prefix_space=__A					)

								_a								= "lower newer"

								# Testing tokenization
								_a								= tokenizer.tokenize(__A					,				add_prefix_space=__A					)
								_a								= rust_tokenizer.tokenize(__A					)
								self.assertListEqual(__A					,				__A					)

								# Testing conversion to ids without special tokens
								_a								= tokenizer.encode(__A					,				add_special_tokens=__A					,				add_prefix_space=__A					)
								_a								= rust_tokenizer.encode(__A					,				add_special_tokens=__A					)
								self.assertListEqual(__A					,				__A					)

								# Testing conversion to ids with special tokens
								_a								= self.get_rust_tokenizer(add_prefix_space=__A					)
								_a								= tokenizer.encode(__A					,				add_prefix_space=__A					)
								_a								= rust_tokenizer.encode(__A					)
								self.assertListEqual(__A					,				__A					)

								# Testing the unknown token
								_a								= tokens + [rust_tokenizer.unk_token]
								_a								= [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A					)					,				__A					)




				def   UpperCamelCase__   (  self     :					Any					,				*__a     :					Union[str, Any]					,				**__a     :					Optional[Any]					):
								# It's very difficult to mix/test pretokenization with byte-level
								# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
								pass




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					Any=15					):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																_a								= self.rust_tokenizer_class.from_pretrained(__A					,				**__A					)

																# Simple input
																_a								= "This is a simple input"
																_a								= ["This is a simple input 1", "This is a simple input 2"]
																_a								= ("This is a simple input", "This is a pair")
																_a								= [
																    ("This is a simple input 1", "This is a simple input 2"),
																    ("This is a simple pair 1", "This is a simple pair 2"),
																]

																# Simple input tests
																self.assertRaises(__A					,				tokenizer_r.encode					,				__A					,				max_length=__A					,				padding="max_length"					)

																# Simple input
																self.assertRaises(__A					,				tokenizer_r.encode_plus					,				__A					,				max_length=__A					,				padding="max_length"					)

																# Simple input
																self.assertRaises(
																    __A					,				tokenizer_r.batch_encode_plus					,				__A					,				max_length=__A					,				padding="max_length"					,				)

																# Pair input
																self.assertRaises(__A					,				tokenizer_r.encode					,				__A					,				max_length=__A					,				padding="max_length"					)

																# Pair input
																self.assertRaises(__A					,				tokenizer_r.encode_plus					,				__A					,				max_length=__A					,				padding="max_length"					)

																# Pair input
																self.assertRaises(
																    __A					,				tokenizer_r.batch_encode_plus					,				__A					,				max_length=__A					,				padding="max_length"					,				)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= GPTaTokenizer.from_pretrained(self.tmpdirname					,				pad_token="<pad>"					)

								# Simple input
								_a								= "This is a simple input"
								_a								= ["This is a simple input looooooooong", "This is a simple input"]
								_a								= ("This is a simple input", "This is a pair")
								_a								= [
								    ("This is a simple input loooooong", "This is a simple input"),
								    ("This is a simple pair loooooong", "This is a simple pair"),
								]

								_a								= tokenizer.pad_token_id

								_a								= tokenizer(__A					,				padding="max_length"					,				max_length=30					,				return_tensors="np"					)
								_a								= tokenizer(__A					,				padding=__A					,				truncate=__A					,				return_tensors="np"					)
								_a								= tokenizer(*__A					,				padding="max_length"					,				max_length=60					,				return_tensors="np"					)
								_a								= tokenizer(__A					,				padding=__A					,				truncate=__A					,				return_tensors="np"					)

								# s
								# test single string max_length padding
								self.assertEqual(out_s["input_ids"].shape[-1]					,				30					)
								self.assertTrue(pad_token_id in out_s["input_ids"]					)
								self.assertTrue(0 in out_s["attention_mask"]					)

								# s2
								# test automatic padding
								self.assertEqual(out_sa["input_ids"].shape[-1]					,				33					)
								# long slice doesn't have padding
								self.assertFalse(pad_token_id in out_sa["input_ids"][0]					)
								self.assertFalse(0 in out_sa["attention_mask"][0]					)
								# short slice does have padding
								self.assertTrue(pad_token_id in out_sa["input_ids"][1]					)
								self.assertTrue(0 in out_sa["attention_mask"][1]					)

								# p
								# test single pair max_length padding
								self.assertEqual(out_p["input_ids"].shape[-1]					,				60					)
								self.assertTrue(pad_token_id in out_p["input_ids"]					)
								self.assertTrue(0 in out_p["attention_mask"]					)

								# p2
								# test automatic padding pair
								self.assertEqual(out_pa["input_ids"].shape[-1]					,				52					)
								# long slice pair doesn't have padding
								self.assertFalse(pad_token_id in out_pa["input_ids"][0]					)
								self.assertFalse(0 in out_pa["attention_mask"][0]					)
								# short slice pair does have padding
								self.assertTrue(pad_token_id in out_pa["input_ids"][1]					)
								self.assertTrue(0 in out_pa["attention_mask"][1]					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= "$$$"
								_a								= GPTaTokenizer.from_pretrained(self.tmpdirname					,				bos_token=__A					,				add_bos_token=__A					)

								_a								= "This is a simple input"
								_a								= ["This is a simple input 1", "This is a simple input 2"]

								_a								= tokenizer.bos_token_id

								_a								= tokenizer(__A					)
								_a								= tokenizer(__A					)

								self.assertEqual(out_s.input_ids[0]					,				__A					)
								self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids					)					)

								_a								= tokenizer.decode(out_s.input_ids					)
								_a								= tokenizer.batch_decode(out_sa.input_ids					)

								self.assertEqual(decode_s.split()[0]					,				__A					)
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa					)					)




				def   UpperCamelCase__   (  self     :					int					):
								pass




				def   UpperCamelCase__   (  self     :					int					):
								# TODO: change to self.get_tokenizers() when the fast version is implemented
								_a								= [self.get_tokenizer(do_lower_case=__A					,				add_bos_token=__A					)]
								for tokenizer in tokenizers:
												with self.subTest(f'{tokenizer.__class__.__name__}'					):
																_a								= "Encode this."
																_a								= "This one too please."
																_a								= tokenizer.encode(__A					,				add_special_tokens=__A					)
																encoded_sequence += tokenizer.encode(__A					,				add_special_tokens=__A					)
																_a								= tokenizer.encode_plus(
																    __A					,				__A					,				add_special_tokens=__A					,				return_special_tokens_mask=__A					,				)
																_a								= encoded_sequence_dict["input_ids"]
																_a								= encoded_sequence_dict["special_tokens_mask"]
																self.assertEqual(len(__A					)					,				len(__A					)					)

																_a								= [
																    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__A					)
																]
																_a								= [x for x in filtered_sequence if x is not None]
																self.assertEqual(__A					,				__A					)


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Tuple					):
								# More context:
								# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
								# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
								# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

								_a								= AutoTokenizer.from_pretrained("facebook/opt-350m"					,				from_slow=__A					)
								_a								= "A photo of a cat"

								_a								= tokenizer.encode(
								    __A					,				)
								self.assertEqual(__A					,				[2, 2_50, 13_45, 9, 10, 47_58]					)
								tokenizer.save_pretrained("test_opt"					)

								_a								= AutoTokenizer.from_pretrained("./test_opt"					)
								_a								= tokenizer.encode(
								    __A					,				)
								self.assertEqual(__A					,				[2, 2_50, 13_45, 9, 10, 47_58]					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= AutoTokenizer.from_pretrained("facebook/opt-350m"					,				use_slow=__A					)
								_a								= "A photo of a cat"

								_a								= tokenizer.encode(
								    __A					,				)
								# Same as above
								self.assertEqual(__A					,				[2, 2_50, 13_45, 9, 10, 47_58]					)




				@unittest.skip("This test is failing because of a bug in the fast tokenizer"					)
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= AutoTokenizer.from_pretrained("facebook/opt-350m"					,				from_slow=__A					)

								_a								= "bos"
								_a								= tokenizer.get_vocab()["bos"]

								_a								= "A photo of a cat"
								_a								= tokenizer.encode(
								    __A					,				)
								# We changed the bos token
								self.assertEqual(__A					,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]					)
								tokenizer.save_pretrained("./tok"					)
								_a								= AutoTokenizer.from_pretrained("./tok"					)
								self.assertTrue(tokenizer.is_fast					)
								_a								= tokenizer.encode(
								    __A					,				)
								self.assertEqual(__A					,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]					)







'''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
