


"""simple docstring"""







import copy
import os
from typing import Union

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


a__       :							Any        =  logging.get_logger(__name__)

a__       :							int        =  {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}




class 				UpperCamelCase_			(				__lowercase):

 """simple docstring"""





 snake_case__       :						List[Any]					      =    'bridgetower_vision_model'




 def __init__(				self    :       int    ,    UpperCAmelCase__    :       Optional[Any]=7_6_8    ,    UpperCAmelCase__    :       str=1_2    ,    UpperCAmelCase__    :       str=3    ,    UpperCAmelCase__    :       Any=1_6    ,    UpperCAmelCase__    :       str=2_8_8    ,    UpperCAmelCase__    :       int=1    ,    UpperCAmelCase__    :       List[Any]=1E-05    ,    UpperCAmelCase__    :       List[str]=False    ,    UpperCAmelCase__    :       str=True    ,    UpperCAmelCase__    :       List[str]=False    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			Optional[int]:
      super().__init__(**__A     )
      __SCREAMING_SNAKE_CASE		     =       hidden_size
      __SCREAMING_SNAKE_CASE		     =       num_hidden_layers
      __SCREAMING_SNAKE_CASE		     =       num_channels
      __SCREAMING_SNAKE_CASE		     =       patch_size
      __SCREAMING_SNAKE_CASE		     =       image_size
      __SCREAMING_SNAKE_CASE		     =       initializer_factor
      __SCREAMING_SNAKE_CASE		     =       layer_norm_eps
      __SCREAMING_SNAKE_CASE		     =       stop_gradient
      __SCREAMING_SNAKE_CASE		     =       share_layernorm
      __SCREAMING_SNAKE_CASE		     =       remove_last_layer




 @classmethod
 def 						UpperCAmelCase_   (				cls    :       str    ,    UpperCAmelCase__    :       str    ,    **UpperCAmelCase__    :       int     ) ->			"PretrainedConfig":
      __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       cls.get_config_dict(__A    ,    **__A     )

      if config_dict.get("model_type"     ) == "bridgetower":
           __SCREAMING_SNAKE_CASE		     =       config_dict["text_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_			(				__lowercase):

 """simple docstring"""





 snake_case__       :						Optional[int]					      =    'bridgetower_text_model'




 def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       Dict=5_0_2_6_5    ,    UpperCAmelCase__    :       int=7_6_8    ,    UpperCAmelCase__    :       int=1_2    ,    UpperCAmelCase__    :       Union[str, Any]=1_2    ,    UpperCAmelCase__    :       int=1    ,    UpperCAmelCase__    :       List[str]=3_0_7_2    ,    UpperCAmelCase__    :       List[Any]="gelu"    ,    UpperCAmelCase__    :       int=0.1    ,    UpperCAmelCase__    :       List[Any]=0.1    ,    UpperCAmelCase__    :       str=5_1_4    ,    UpperCAmelCase__    :       Any=1    ,    UpperCAmelCase__    :       Any=1E-05    ,    UpperCAmelCase__    :       Union[str, Any]=1    ,    UpperCAmelCase__    :       Any=0    ,    UpperCAmelCase__    :       List[str]=2    ,    UpperCAmelCase__    :       List[str]="absolute"    ,    UpperCAmelCase__    :       Optional[Any]=True    ,    **UpperCAmelCase__    :       Tuple    ,    ) ->			Dict:
      super().__init__(**__A     )

      __SCREAMING_SNAKE_CASE		     =       vocab_size
      __SCREAMING_SNAKE_CASE		     =       hidden_size
      __SCREAMING_SNAKE_CASE		     =       num_hidden_layers
      __SCREAMING_SNAKE_CASE		     =       num_attention_heads
      __SCREAMING_SNAKE_CASE		     =       hidden_act
      __SCREAMING_SNAKE_CASE		     =       initializer_factor
      __SCREAMING_SNAKE_CASE		     =       intermediate_size
      __SCREAMING_SNAKE_CASE		     =       hidden_dropout_prob
      __SCREAMING_SNAKE_CASE		     =       attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE		     =       max_position_embeddings
      __SCREAMING_SNAKE_CASE		     =       type_vocab_size
      __SCREAMING_SNAKE_CASE		     =       layer_norm_eps
      __SCREAMING_SNAKE_CASE		     =       position_embedding_type
      __SCREAMING_SNAKE_CASE		     =       use_cache
      __SCREAMING_SNAKE_CASE		     =       pad_token_id
      __SCREAMING_SNAKE_CASE		     =       bos_token_id
      __SCREAMING_SNAKE_CASE		     =       eos_token_id




 @classmethod
 def 						UpperCAmelCase_   (				cls    :       Union[str, Any]    ,    UpperCAmelCase__    :       int    ,    **UpperCAmelCase__    :       int     ) ->			"PretrainedConfig":
      __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       cls.get_config_dict(__A    ,    **__A     )

      if config_dict.get("model_type"     ) == "bridgetower":
           __SCREAMING_SNAKE_CASE		     =       config_dict["text_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_			(				__lowercase):

 """simple docstring"""





 snake_case__       :						Dict					      =    'bridgetower'




 def __init__(				self    :       Dict    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       List[str]="gelu"    ,    UpperCAmelCase__    :       Union[str, Any]=7_6_8    ,    UpperCAmelCase__    :       Dict=1    ,    UpperCAmelCase__    :       int=1E-05    ,    UpperCAmelCase__    :       List[str]=False    ,    UpperCAmelCase__    :       str="add"    ,    UpperCAmelCase__    :       Tuple=1_2    ,    UpperCAmelCase__    :       Optional[int]=6    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       List[str]=False    ,    UpperCAmelCase__    :       List[Any]=None    ,    UpperCAmelCase__    :       Optional[int]=None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			Any:
      # TODO: remove this once the Hub files are updated.
      __SCREAMING_SNAKE_CASE		     =       kwargs.pop("text_config_dict"    ,    __A     )
      __SCREAMING_SNAKE_CASE		     =       kwargs.pop("vision_config_dict"    ,    __A     )

      super().__init__(**__A     )
      __SCREAMING_SNAKE_CASE		     =       share_cross_modal_transformer_layers
      __SCREAMING_SNAKE_CASE		     =       hidden_act
      __SCREAMING_SNAKE_CASE		     =       hidden_size
      __SCREAMING_SNAKE_CASE		     =       initializer_factor
      __SCREAMING_SNAKE_CASE		     =       layer_norm_eps
      __SCREAMING_SNAKE_CASE		     =       share_link_tower_layers
      __SCREAMING_SNAKE_CASE		     =       link_tower_type
      __SCREAMING_SNAKE_CASE		     =       num_attention_heads
      __SCREAMING_SNAKE_CASE		     =       num_hidden_layers
      __SCREAMING_SNAKE_CASE		     =       tie_word_embeddings
      __SCREAMING_SNAKE_CASE		     =       init_layernorm_from_vision_encoder

      if text_config is None:
           __SCREAMING_SNAKE_CASE		     =       {}
           logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."     )

      if vision_config is None:
           __SCREAMING_SNAKE_CASE		     =       {}
           logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."     )

      __SCREAMING_SNAKE_CASE		     =       BridgeTowerTextConfig(**__A     )
      __SCREAMING_SNAKE_CASE		     =       BridgeTowerVisionConfig(**__A     )




 @classmethod
 def 						UpperCAmelCase_   (				cls    :       str    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       List[str]    ,    **UpperCAmelCase__    :       Union[str, Any]     ) ->			List[Any]:
      return cls(text_config=text_config.to_dict()    ,    vision_config=vision_config.to_dict()    ,    **__A     )




 def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[Any]:
      __SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE		     =       self.text_config.to_dict()
      __SCREAMING_SNAKE_CASE		     =       self.vision_config.to_dict()
      __SCREAMING_SNAKE_CASE		     =       self.__class__.model_type
      return output




"""simple docstring"""







import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"    ,						"False")) is not True    ,						reason="Skipping test because should only be run when releasing minor transformers version"    ,						)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Any:
						if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()    ,    encoding="utf-8"    ,    check=UpperCAmelCase__    ,    )
						assert hasattr(self    ,    "env"     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						__SCREAMING_SNAKE_CASE		     =       {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

						# creates estimator
						return HuggingFace(
						    entry_point=self.script    ,    source_dir=self.env.test_path    ,    role=self.env.role    ,    image_uri=self.env.image_uri    ,    base_job_name=UpperCAmelCase__    ,    instance_count=UpperCAmelCase__    ,    instance_type=self.instance_type    ,    debugger_hook_config=UpperCAmelCase__    ,    hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,    metric_definitions=self.env.metric_definitions    ,    distribution=UpperCAmelCase__    ,    py_version="py36"    ,    )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Optional[Any]:
						TrainingJobAnalytics(UpperCAmelCase__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




	@parameterized.expand([(2,)]     )
	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			List[str]:
						# create estimator
						__SCREAMING_SNAKE_CASE		     =       self.create_estimator(UpperCAmelCase__     )

						# run training
						estimator.fit()

						# result dataframe
						__SCREAMING_SNAKE_CASE		     =       TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

						# extract kpis
						__SCREAMING_SNAKE_CASE		     =       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
						__SCREAMING_SNAKE_CASE		     =       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__SCREAMING_SNAKE_CASE		     =       (
						    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"    ,    9_9_9_9_9_9     )
						)

						# assert kpis
						assert train_runtime <= self.results["train_runtime"]
						assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy     )
						assert all(t <= self.results["eval_loss"] for t in eval_loss     )

						# dump tests result into json file to share in PR
						with open(F"""{estimator.latest_training_job.name}.json"""    ,    "w"     ) as outfile:
											json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}    ,    UpperCAmelCase__     )
