

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						OpenAIGPTTokenizer
			SCREAMING_SNAKE_CASE							   =						OpenAIGPTTokenizerFast
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						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',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    '<unk>',
						]
						__a        =dict(zip(__snake_case					,  range(len(__snake_case					)					)					)					)
						__a        =['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

						__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'					) as fp:
									fp.write(json.dumps(__snake_case					)					)
						with open(self.merges_file					,  'w'					) as fp:
									fp.write('\n'.join(__snake_case					)					)





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




						'''simple docstring'''
						return "lower newer", "lower newer"





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =OpenAIGPTTokenizer(self.vocab_file					,  self.merges_file					)

						__a        ='lower'
						__a        =['low', 'er</w>']
						__a        =tokenizer.tokenize(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =tokens + ['<unk>']
						__a        =[14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  __snake_case					)





			def 		__magic_name__    (		self					,  __snake_case=15					)   -> List[str]:




						'''simple docstring'''
						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(__snake_case					,  **__snake_case					)

												# 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(__snake_case					,  tokenizer_r.encode					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					)

												# Simple input
												self.assertRaises(__snake_case					,  tokenizer_r.encode_plus					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					)

												# Simple input
												self.assertRaises(
												    __snake_case					,  tokenizer_r.batch_encode_plus					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					,  )

												# Pair input
												self.assertRaises(__snake_case					,  tokenizer_r.encode					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					)

												# Pair input
												self.assertRaises(__snake_case					,  tokenizer_r.encode_plus					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					)

												# Pair input
												self.assertRaises(
												    __snake_case					,  tokenizer_r.batch_encode_plus					,  __snake_case					,  max_length=__snake_case					,  padding='max_length'					,  )





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						pass






@require_ftfy
@require_spacy
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_      ):
			pass









import copy
import os
from typing import Union

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


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =num_channels
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from GITConfig
						if config_dict.get('model_type'					) == "git":
									__a        =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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

						__a        =GitVisionConfig(**__snake_case					)
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =use_cache
						__a        =tie_word_embeddings
						__a        =num_image_with_embedding

						__a        =bos_token_id
						__a        =eos_token_id





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






