
import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin
class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	DownBlockaD  # noqa F405
	a			:Any         =	'down'
	def 						_lowercase  (       self  :			Dict       )       ->     str:
					lowercase_       =					[-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:int         =	ResnetDownsampleBlockaD  # noqa F405
	a			:Dict         =	'down'
	def 						_lowercase  (       self  :			Dict       )       ->     int:
					lowercase_       =					[0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:int         =	AttnDownBlockaD  # noqa F405
	a			:Tuple         =	'down'
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[Any]:
					lowercase_       =					[0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:str         =	CrossAttnDownBlockaD  # noqa F405
	a			:str         =	'down'
	def 						_lowercase  (       self  :			List[Any]       )       ->     Optional[Any]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			List[Any]       )       ->     Dict:
					lowercase_       =					[0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:List[str]         =	SimpleCrossAttnDownBlockaD  # noqa F405
	a			:List[Any]         =	'down'
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     Dict:
					return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	@unittest.skipIf(torch_device == '''mps'''       ,							'''MPS result is not consistent'''       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					[0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Dict         =	SkipDownBlockaD  # noqa F405
	a			:str         =	'down'
	@property
	def 						_lowercase  (       self  :			int       )       ->     Optional[int]:
					return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       )       ->     List[str]:
					lowercase_       =					[-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	AttnSkipDownBlockaD  # noqa F405
	a			:Optional[Any]         =	'down'
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     List[str]:
					return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					lowercase_       =					[0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Dict         =	DownEncoderBlockaD  # noqa F405
	a			:Tuple         =	'down'
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Optional[int]:
					return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			str       )       ->     Dict:
					lowercase_       =					{
					    '''in_channels''': 3_2,
					    '''out_channels''': 3_2,
					}
					lowercase_       =					self.dummy_input
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			List[Any]       )       ->     Tuple:
					lowercase_       =					[1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[int]         =	AttnDownEncoderBlockaD  # noqa F405
	a			:Optional[Any]         =	'down'
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     Dict:
					return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					{
					    '''in_channels''': 3_2,
					    '''out_channels''': 3_2,
					}
					lowercase_       =					self.dummy_input
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			str       )       ->     Any:
					lowercase_       =					[0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Dict         =	UNetMidBlockaD  # noqa F405
	a			:str         =	'mid'
	def 						_lowercase  (       self  :			Any       )       ->     int:
					lowercase_       =					{
					    '''in_channels''': 3_2,
					    '''temb_channels''': 1_2_8,
					}
					lowercase_       =					self.dummy_input
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Any:
					lowercase_       =					[-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:List[Any]         =	UNetMidBlockaDCrossAttn  # noqa F405
	a			:str         =	'mid'
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Any       )       ->     str:
					lowercase_       =					[0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:str         =	UNetMidBlockaDSimpleCrossAttn  # noqa F405
	a			:List[str]         =	'mid'
	@property
	def 						_lowercase  (       self  :			Any       )       ->     int:
					return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Tuple       )       ->     int:
					lowercase_       =					[0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:str         =	UpBlockaD  # noqa F405
	a			:Optional[int]         =	'up'
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     Dict:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					lowercase_       =					[-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	ResnetUpsampleBlockaD  # noqa F405
	a			:Tuple         =	'up'
	@property
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					[0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Any         =	CrossAttnUpBlockaD  # noqa F405
	a			:Optional[Any]         =	'up'
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[int]:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     Optional[Any]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[int]:
					lowercase_       =					[-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	SimpleCrossAttnUpBlockaD  # noqa F405
	a			:List[str]         =	'up'
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       ,							include_encoder_hidden_states=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			str       )       ->     List[str]:
					lowercase_  ,	lowercase_       =					super().prepare_init_args_and_inputs_for_common()
					lowercase_       =					3_2
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Dict       )       ->     Any:
					lowercase_       =					[0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[int]         =	AttnUpBlockaD  # noqa F405
	a			:Tuple         =	'up'
	@property
	def 						_lowercase  (       self  :			Any       )       ->     str:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )




	@unittest.skipIf(torch_device == '''mps'''       ,							'''MPS result is not consistent'''       )
	def 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					[0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[int]         =	SkipUpBlockaD  # noqa F405
	a			:Tuple         =	'up'
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     Any:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					lowercase_       =					[-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	AttnSkipUpBlockaD  # noqa F405
	a			:List[Any]         =	'up'
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Tuple:
					return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					lowercase_       =					[0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Any         =	UpDecoderBlockaD  # noqa F405
	a			:Optional[Any]         =	'up'
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     Union[str, Any]:
					return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			str       )       ->     Tuple:
					lowercase_       =					{'''in_channels''': 3_2, '''out_channels''': 3_2}

					lowercase_       =					self.dummy_input
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			int       )       ->     Tuple:
					lowercase_       =					[0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
					super().test_output(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:List[Any]         =	AttnUpDecoderBlockaD  # noqa F405
	a			:List[str]         =	'up'
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Optional[int]       )       ->     str:
					lowercase_       =					{'''in_channels''': 3_2, '''out_channels''': 3_2}

					lowercase_       =					self.dummy_input
					return init_dict, inputs_dict




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[Any]:
					lowercase_       =					[0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
					super().test_output(SCREAMING_SNAKE_CASE_       )






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : PreTrainedTokenizer				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int] = None				,					):
       snake_case       :   int							=      {}

       if train_file is not None:
              snake_case       :   List[Any]							=      [train_file]
       if eval_file is not None:
              snake_case       :   Optional[int]							=      [eval_file]
       if test_file is not None:
              snake_case       :   Any							=      [test_file]

       snake_case       :   int							=      datasets.load_dataset("csv"				,					data_files=__lowerCamelCase    )
       snake_case       :   str							=      list(ds[list(files.keys()    )[0]].features.keys()    )
       snake_case       :   int							=      features_name.pop(__lowerCamelCase    )
       snake_case       :   str							=      list(set(ds[list(files.keys()    )[0]][label_name]    )    )
       snake_case       :   str							=      {label: i for i, label in enumerate(__lowerCamelCase    )}
       snake_case       :   List[Any]							=      tokenizer.model_input_names
       snake_case       :   List[Any]							=      {}

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             example[features_name[0]]				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"    )				,					batched=__lowerCamelCase				,					)
       elif len(__lowerCamelCase    ) == 2:
              for k in files.keys():
                     snake_case       :   List[Any]							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"				,					)				,					batched=__lowerCamelCase				,					)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     snake_case       :   Dict							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Union[str, Any]							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Any							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   List[str]							=      labelaid[ex[label_name]]
                     yield (d, label)

       snake_case       :   int							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              snake_case       :   Optional[Any]							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       snake_case       :   Tuple							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              snake_case       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              snake_case       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the test file"} )
   A__					:		int						     =     field(
       default=1_28       ,metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }       ,)
   A__					:		bool						     =     field(
       default=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






@dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
   A__					:		bool						     =     field(default=A_       ,metadata={"help": "Set this flag to use fast tokenization."} )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)



def 			UpperCamelCase					(    ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       snake_case       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   int							=      parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir    )
           and os.listdir(training_args.output_dir    )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  " --overwrite_output_dir to overcome."    )

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,					datefmt="%m/%d/%Y %H:%M:%S"				,					level=logging.INFO				,					)
       logger.info(
           f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, """
           f"""16-bits training: {training_args.fpaa}"""    )
       logger.info(f"""Training/evaluation parameters {training_args}"""    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,					cache_dir=model_args.cache_dir				,					)

       snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      get_tfds(
           train_file=data_args.train_file				,					eval_file=data_args.dev_file				,					test_file=data_args.test_file				,					tokenizer=__lowerCamelCase				,					label_column_id=data_args.label_column_id				,					max_seq_length=data_args.max_seq_length				,					)

       snake_case       :   Optional[Any]							=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=len(__lowerCamelCase    )				,					labelaid=__lowerCamelCase				,					idalabel={id: label for label, id in labelaid.items()}				,					finetuning_task="text-classification"				,					cache_dir=model_args.cache_dir				,					)

       with training_args.strategy.scope():
              snake_case       :   int							=      TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,					from_pt=bool(".bin" in model_args.model_name_or_path    )				,					config=__lowerCamelCase				,					cache_dir=model_args.cache_dir				,					)

       def compute_metrics(__lowerCamelCase     : EvalPrediction    ) -> Dict:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       snake_case       :   int							=      TFTrainer(
           model=__lowerCamelCase				,					args=__lowerCamelCase				,					train_dataset=__lowerCamelCase				,					eval_dataset=__lowerCamelCase				,					compute_metrics=__lowerCamelCase				,					)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir    )

       # Evaluation
       snake_case       :   int							=      {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              snake_case       :   Any							=      trainer.evaluate()
              snake_case       :   List[Any]							=      os.path.join(training_args.output_dir				,					"eval_results.txt"    )

              with open(__lowerCamelCase				,					"w"    ) as writer:
                     logger.info("***** Eval results *****"    )

                     for key, value in result.items():
                            logger.info(f"""  {key} = {value}"""    )
                            writer.write(f"""{key} = {value}\n"""    )

                     results.update(__lowerCamelCase    )

       return results


if __name__ == "__main__":
   main()





