





'''simple docstring'''




import copy
import os
from typing import Union

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


_A:					int							=				logging.get_logger(__name__)

_A:					Tuple							=				{
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Any						   =       """git_vision_model"""



    def __init__(  self     ,			__A=768     ,			__A=3_072     ,			__A=12     ,			__A=12     ,			__A=3     ,			__A=224     ,			__A=16     ,			__A="quick_gelu"     ,			__A=1E-5     ,			__A=0.0     ,			__A=0.0_2     ,			**__A     ,			):
     super().__init__(**__A					)

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




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			**__A					):
     cls._set_token_in_kwargs(__A					)

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

     # get the vision config dict if we are loading from GITConfig
     if config_dict.get('model_type'					) == "git":
      __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 UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			str						   =       """git"""



    def __init__(  self     ,			__A=None     ,			__A=30_522     ,			__A=768     ,			__A=6     ,			__A=12     ,			__A=3_072     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=1_024     ,			__A=0.0_2     ,			__A=1E-12     ,			__A=0     ,			__A="absolute"     ,			__A=True     ,			__A=False     ,			__A=101     ,			__A=102     ,			__A=None     ,			**__A     ,			):
     super().__init__(bos_token_id=__A     ,			eos_token_id=__A     ,			pad_token_id=__A     ,			**__A					)

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

     __UpperCAmelCase					=   GitVisionConfig(**__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					=   initializer_range
     __UpperCAmelCase					=   layer_norm_eps
     __UpperCAmelCase					=   position_embedding_type
     __UpperCAmelCase					=   use_cache
     __UpperCAmelCase					=   tie_word_embeddings
     __UpperCAmelCase					=   num_image_with_embedding

     __UpperCAmelCase					=   bos_token_id
     __UpperCAmelCase					=   eos_token_id




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   copy.deepcopy(self.__dict__					)
     __UpperCAmelCase					=   self.vision_config.to_dict()
     __UpperCAmelCase					=   self.__class__.model_type
     return output












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





