'''simple docstring'''




import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm
  import run_image_classification
  import run_mlm
  import run_ner
  import run_qa as run_squad
  import run_summarization
  import run_swag
  import run_text_classification
  import run_translation


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE :  int  =						logging.getLogger()

def 			UpperCamelCase_							(      )					->					List[str]:


       """simple docstring"""







       _UpperCAmelCase  :						Any							   =					argparse.ArgumentParser()
       parser.add_argument("-f"					)
       _UpperCAmelCase  :						Union[str, Any]							   =					parser.parse_args()
       return args.f

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]					)					->					Any:


       """simple docstring"""







       _UpperCAmelCase  :						Dict							   =					{}
       _UpperCAmelCase  :						List[str]							   =					os.path.join(_UpperCAmelCase    ,			"all_results.json"					)
       if os.path.exists(_UpperCAmelCase					):
              with open(_UpperCAmelCase    ,			"r"					) as f:
                     _UpperCAmelCase  :						Optional[Any]							   =					json.load(_UpperCAmelCase					)
       else:
              raise ValueError(F"""can't find {path}"""					)
       return results

def 			UpperCamelCase_							(      )					->					List[Any]:


       """simple docstring"""







       return bool(tf.config.list_physical_devices("GPU"					)					)


__SCREAMING_SNAKE_CASE :  List[Any]  =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 			lowerCamelCase_			(__lowerCamelCase					):
      '''simple docstring'''

      @skip("Skipping until shape inference for to_tf_dataset PR is merged." )
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						List[str]							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						str							   =					F"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             if is_cuda_available():
                    testargs.append("--fp16" )

             with patch.object(A		,      "argv"		,      A ):
                    run_text_classification.main()
                    # Reset the mixed precision policy so we don't break other tests
                    tf.keras.mixed_precision.set_global_policy("float32" )
                    _UpperCAmelCase  :						Any							   =					get_results(A )
                    self.assertGreaterEqual(result["eval_accuracy"]		,      0.75 )




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						int							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						Any							   =					F"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             if len(tf.config.list_physical_devices("GPU" ) ) > 1:
                    # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                    return

             with patch.object(A		,      "argv"		,      A ):
                    run_clm.main()
                    _UpperCAmelCase  :						List[Any]							   =					get_results(A )
                    self.assertLess(result["eval_perplexity"]		,      100 )




      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Tuple							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						Dict							   =					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_mlm.main()
                    _UpperCAmelCase  :						Tuple							   =					get_results(A )
                    self.assertLess(result["eval_perplexity"]		,      42 )




      def   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						Optional[Any]							   =					7 if get_gpu_count() > 1 else 2

             _UpperCAmelCase  :						int							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						Any							   =					F"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_ner.main()
                    _UpperCAmelCase  :						Dict							   =					get_results(A )
                    self.assertGreaterEqual(result["accuracy"]		,      0.75 )




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						int							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						Tuple							   =					F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_squad.main()
                    _UpperCAmelCase  :						Tuple							   =					get_results(A )
                    self.assertGreaterEqual(result["f1"]		,      30 )
                    self.assertGreaterEqual(result["exact"]		,      30 )




      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						int							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						str							   =					F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_swag.main()
                    _UpperCAmelCase  :						str							   =					get_results(A )
                    self.assertGreaterEqual(result["val_accuracy"]		,      0.8 )




      @slow
      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Dict							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						List[str]							   =					F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_summarization.main()
                    _UpperCAmelCase  :						Union[str, Any]							   =					get_results(A )
                    self.assertGreaterEqual(result["rouge1"]		,      10 )
                    self.assertGreaterEqual(result["rouge2"]		,      2 )
                    self.assertGreaterEqual(result["rougeL"]		,      7 )
                    self.assertGreaterEqual(result["rougeLsum"]		,      7 )




      @slow
      def   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						List[Any]							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						str							   =					F"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_translation.main()
                    _UpperCAmelCase  :						Dict							   =					get_results(A )
                    self.assertGreaterEqual(result["bleu"]		,      30 )





      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						Dict							   =					F"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

             with patch.object(A		,      "argv"		,      A ):
                    run_image_classification.main()
                    _UpperCAmelCase  :						Union[str, Any]							   =					get_results(A )
                    self.assertGreaterEqual(result["accuracy"]		,      0.7 )


'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

