






"""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


UpperCamelCase					=     [
    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)

UpperCamelCase					=     logging.getLogger()






def 							A(    ):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     argparse.ArgumentParser()
       parser.add_argument("-f"						)
       lowercase__:							Any         =     parser.parse_args()
       return args.f






def 							A(    snake_case_						):
       """simple docstring"""





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






def 							A(    ):
       """simple docstring"""





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


UpperCamelCase					=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @skip("Skipping until shape inference for to_tf_dataset PR is merged.")
   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Dict         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_text_classification.main()
                 # Reset the mixed precision policy so we don't break other tests
                 tf.keras.mixed_precision.set_global_policy("float32")
                 lowercase__:							Dict         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["eval_accuracy"]					, 0.75)





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Union[str, 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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_clm.main()
                 lowercase__:							List[Any]         =     get_results(UpperCAmelCase_)
                 self.assertLess(result["eval_perplexity"]					, 100)





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Tuple         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[str]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_mlm.main()
                 lowercase__:							Optional[Any]         =     get_results(UpperCAmelCase_)
                 self.assertLess(result["eval_perplexity"]					, 42)





   def __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							List[Any]         =     7 if get_gpu_count() > 1 else 2

          lowercase__:							Union[str, Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Tuple         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_ner.main()
                 lowercase__:							Optional[int]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["accuracy"]					, 0.75)





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




          '''simple docstring'''




          lowercase__:							List[Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[str]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_squad.main()
                 lowercase__:							Union[str, Any]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["f1"]					, 30)
                 self.assertGreaterEqual(result["exact"]					, 30)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							Tuple         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_swag.main()
                 lowercase__:							Optional[int]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["val_accuracy"]					, 0.8)





   @slow
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[Any]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_summarization.main()
                 lowercase__:							int         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["rouge1"]					, 10)
                 self.assertGreaterEqual(result["rouge2"]					, 2)
                 self.assertGreaterEqual(result["rougeL"]					, 7)
                 self.assertGreaterEqual(result["rougeLsum"]					, 7)





   @slow
   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Dict         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Optional[Any]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_translation.main()
                 lowercase__:							List[str]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["bleu"]					, 30)





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							int         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Optional[Any]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_image_classification.main()
                 lowercase__:							str         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["accuracy"]					, 0.7)







"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
