
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor





class 				A__						:
      def __init__( self       ,    __magic_name__       ,    __magic_name__=1_3       ,    __magic_name__=3_0       ,    __magic_name__=2       ,    __magic_name__=3       ,    __magic_name__=True       ,    __magic_name__=True       ,    __magic_name__=3_2       ,    __magic_name__=2       ,    __magic_name__=4       ,    __magic_name__=3_7       ,    __magic_name__="gelu"       ,    __magic_name__=0.1       ,    __magic_name__=0.1       ,    __magic_name__=1_0       ,    __magic_name__=0.02       ,    __magic_name__=3       ,    __magic_name__=0.6       ,    __magic_name__=None       ,    ):
       lowerCamelCase						:      Tuple							     = parent
       lowerCamelCase						:      List[str]							     = batch_size
       lowerCamelCase						:      Any							     = image_size
       lowerCamelCase						:      List[str]							     = patch_size
       lowerCamelCase						:      Optional[int]							     = num_channels
       lowerCamelCase						:      int							     = is_training
       lowerCamelCase						:      Tuple							     = use_labels
       lowerCamelCase						:      List[Any]							     = hidden_size
       lowerCamelCase						:      Dict							     = num_hidden_layers
       lowerCamelCase						:      Union[str, Any]							     = num_attention_heads
       lowerCamelCase						:      Any							     = intermediate_size
       lowerCamelCase						:      List[Any]							     = hidden_act
       lowerCamelCase						:      Tuple							     = hidden_dropout_prob
       lowerCamelCase						:      str							     = attention_probs_dropout_prob
       lowerCamelCase						:      int							     = type_sequence_label_size
       lowerCamelCase						:      Dict							     = initializer_range
       lowerCamelCase						:      Optional[Any]							     = mask_ratio
       lowerCamelCase						:      Any							     = scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       lowerCamelCase						:      int							     = (image_size // patch_size) ** 2
       lowerCamelCase						:      List[Any]							     = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[Any]							     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       lowerCamelCase						:      List[str]							     = None
       if self.use_labels:
        lowerCamelCase						:      Tuple							     = ids_tensor([self.batch_size]       ,    self.type_sequence_label_size )

       lowerCamelCase						:      Dict							     = self.get_config()

       return config, pixel_values, labels
      def 		UpperCamelCase__				( self ):
       return ViTMAEConfig(
           image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    intermediate_size=self.intermediate_size       ,    decoder_hidden_size=self.hidden_size       ,    decoder_num_hidden_layers=self.num_hidden_layers       ,    decoder_num_attention_heads=self.num_attention_heads       ,    decoder_intermediate_size=self.intermediate_size       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    is_decoder=__lowerCAmelCase       ,    initializer_range=self.initializer_range       ,    mask_ratio=self.mask_ratio       ,    )
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__ ):
       lowerCamelCase						:      Union[str, Any]							     = TFViTMAEModel(config=__lowerCAmelCase )
       lowerCamelCase						:      Union[str, Any]							     = model(__lowerCAmelCase       ,    training=__lowerCAmelCase )
       self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size) )
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__ ):
       lowerCamelCase						:      Any							     = TFViTMAEForPreTraining(__lowerCAmelCase )
       lowerCamelCase						:      List[str]							     = model(__lowerCAmelCase       ,    training=__lowerCAmelCase )
       # expected sequence length = num_patches
       lowerCamelCase						:      List[str]							     = (self.image_size // self.patch_size) ** 2
       lowerCamelCase						:      str							     = self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, num_patches, expected_num_channels) )

       # test greyscale images
       lowerCamelCase						:      Optional[Any]							     = 1
       lowerCamelCase						:      Union[str, Any]							     = TFViTMAEForPreTraining(__lowerCAmelCase )

       lowerCamelCase						:      str							     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       lowerCamelCase						:      Union[str, Any]							     = model(__lowerCAmelCase       ,    training=__lowerCAmelCase )
       lowerCamelCase						:      List[str]							     = self.patch_size**2
       self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, num_patches, expected_num_channels) )






      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = self.prepare_config_and_inputs()
       (lowerCamelCase)						:      Tuple							     = config_and_inputs
       lowerCamelCase						:      Optional[int]							     = {'''pixel_values''': pixel_values}
       return config, inputs_dict





@require_tf
class 				A__						(      __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  unittest.TestCase):
      _UpperCAmelCase							:       Tuple       			=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
      _UpperCAmelCase							:       Optional[Any]       			=     {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

      _UpperCAmelCase							:       Dict       			=     False
      _UpperCAmelCase							:       Union[str, Any]       			=     False
      _UpperCAmelCase							:       Union[str, Any]       			=     False
      _UpperCAmelCase							:       Tuple       			=     False
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = TFViTMAEModelTester(self )
       lowerCamelCase						:      Optional[Any]							     = ConfigTester(self       ,    config_class=__lowerCAmelCase       ,    has_text_modality=__lowerCAmelCase       ,    hidden_size=3_7 )
      def 		UpperCamelCase__				( self ):
       self.config_tester.run_common_tests()
      @unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
      def 		UpperCamelCase__				( self ):
       pass
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Optional[int]							     = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase						:      Dict							     = model_class(__lowerCAmelCase )
        self.assertIsInstance(model.get_input_embeddings()       ,    (tf.keras.layers.Layer) )
        lowerCamelCase						:      Dict							     = model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,    tf.keras.layers.Layer ) )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Optional[Any]							     = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase						:      Optional[int]							     = model_class(__lowerCAmelCase )
        lowerCamelCase						:      Dict							     = inspect.signature(model.call )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase						:      Optional[Any]							     = [*signature.parameters.keys()]

        lowerCamelCase						:      Union[str, Any]							     = ['''pixel_values''']
        self.assertListEqual(arg_names[:1]       ,    __lowerCAmelCase )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Any							     = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase )
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      List[Any]							     = self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase						:      Any							     = int((config.image_size // config.patch_size) ** 2 )
       lowerCamelCase						:      str							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

       for model_class in self.all_model_classes:
        lowerCamelCase						:      int							     = model_class(__lowerCAmelCase )
        lowerCamelCase						:      str							     = self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase )

        lowerCamelCase						:      str							     = model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )

        lowerCamelCase						:      str							     = copy.deepcopy(self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase ) )
        lowerCamelCase						:      Optional[Any]							     = model(**__lowerCAmelCase       ,    noise=__lowerCAmelCase )
        lowerCamelCase						:      Any							     = outputs_dict[0].numpy()
        lowerCamelCase						:      Any							     = outputs_keywords[0].numpy()

        self.assertLess(np.sum(np.abs(output_dict - output_keywords ) )       ,    1e-6 )
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      Any							     = self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase						:      str							     = int((config.image_size // config.patch_size) ** 2 )
       lowerCamelCase						:      Optional[Any]							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

       def prepare_numpy_arrays(__magic_name__ ):
        lowerCamelCase						:      Dict							     = {}
        for k, v in inputs_dict.items():
         if tf.is_tensor(__lowerCAmelCase ):
          lowerCamelCase						:      Optional[int]							     = v.numpy()
         else:
          lowerCamelCase						:      Union[str, Any]							     = np.array(__lowerCAmelCase )

        return inputs_np_dict

       for model_class in self.all_model_classes:
        lowerCamelCase						:      Union[str, Any]							     = model_class(__lowerCAmelCase )

        lowerCamelCase						:      Optional[Any]							     = self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase )
        lowerCamelCase						:      Any							     = prepare_numpy_arrays(__lowerCAmelCase )

        lowerCamelCase						:      Dict							     = model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )
        lowerCamelCase						:      Dict							     = model(**__lowerCAmelCase       ,    noise=__lowerCAmelCase )
        self.assert_outputs_same(__lowerCAmelCase       ,    __lowerCAmelCase )
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__ ):
       np.random.seed(2 )

       lowerCamelCase						:      Tuple							     = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
       lowerCamelCase						:      Optional[int]							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
       lowerCamelCase						:      List[str]							     = tf.constant(__lowerCAmelCase )

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       lowerCamelCase						:      List[Any]							     = tf_noise

       super().check_pt_tf_models(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase )
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      Union[str, Any]							     = self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase						:      int							     = {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__ ),)
           for module_member_name in dir(__lowerCAmelCase )
           if module_member_name.endswith("""MainLayer""" )
           # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
           and module_member_name[: -len("""MainLayer""" )] == model_class.__name__[: -len("""Model""" )]
           for module_member in (getattr(__lowerCAmelCase       ,    __lowerCAmelCase ),)
           if isinstance(__lowerCAmelCase       ,    __lowerCAmelCase )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(__lowerCAmelCase       ,    """_keras_serializable"""       ,    __lowerCAmelCase )
       }

       lowerCamelCase						:      Dict							     = int((config.image_size // config.patch_size) ** 2 )
       lowerCamelCase						:      Optional[int]							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
       lowerCamelCase						:      Dict							     = tf.convert_to_tensor(__lowerCAmelCase )
       inputs_dict.update({"""noise""": noise} )

       for main_layer_class in tf_main_layer_classes:
        lowerCamelCase						:      str							     = main_layer_class(__lowerCAmelCase )

        lowerCamelCase						:      Optional[Any]							     = {
            name: tf.keras.Input(tensor.shape[1:]       ,    dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
        }

        lowerCamelCase						:      List[str]							     = tf.keras.Model(__lowerCAmelCase       ,    outputs=main_layer(__lowerCAmelCase ) )
        lowerCamelCase						:      Tuple							     = model(__lowerCAmelCase )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase						:      List[Any]							     = os.path.join(__lowerCAmelCase       ,    """keras_model.h5""" )
         model.save(__lowerCAmelCase )
         lowerCamelCase						:      Dict							     = tf.keras.models.load_model(
             __lowerCAmelCase       ,    custom_objects={main_layer_class.__name__: main_layer_class} )
         assert isinstance(__lowerCAmelCase       ,    tf.keras.Model )
         lowerCamelCase						:      int							     = model(__lowerCAmelCase )
         self.assert_outputs_same(__lowerCAmelCase       ,    __lowerCAmelCase )
      @slow
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      List[str]							     = self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase						:      Dict							     = int((config.image_size // config.patch_size) ** 2 )
       lowerCamelCase						:      List[Any]							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

       for model_class in self.all_model_classes:
        lowerCamelCase						:      Tuple							     = model_class(__lowerCAmelCase )
        lowerCamelCase						:      str							     = self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase )
        lowerCamelCase						:      List[Any]							     = model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )

        if model_class.__name__ == "TFViTMAEModel":
         lowerCamelCase						:      Dict							     = outputs.last_hidden_state.numpy()
         lowerCamelCase						:      Any							     = 0
        else:
         lowerCamelCase						:      Dict							     = outputs.logits.numpy()
         lowerCamelCase						:      Union[str, Any]							     = 0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__lowerCAmelCase       ,    saved_model=__lowerCAmelCase )
         lowerCamelCase						:      List[str]							     = model_class.from_pretrained(__lowerCAmelCase )
         lowerCamelCase						:      int							     = model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )

         if model_class.__name__ == "TFViTMAEModel":
          lowerCamelCase						:      Optional[int]							     = after_outputs['''last_hidden_state'''].numpy()
          lowerCamelCase						:      str							     = 0
         else:
          lowerCamelCase						:      str							     = after_outputs['''logits'''].numpy()
          lowerCamelCase						:      Optional[Any]							     = 0

         lowerCamelCase						:      int							     = np.amax(np.abs(out_a - out_a ) )
         self.assertLessEqual(__lowerCAmelCase       ,    1e-5 )
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      Dict							     = self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase						:      List[str]							     = int((config.image_size // config.patch_size) ** 2 )
       lowerCamelCase						:      List[str]							     = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

       for model_class in self.all_model_classes:
        lowerCamelCase						:      Union[str, Any]							     = model_class(__lowerCAmelCase )
        lowerCamelCase						:      Union[str, Any]							     = self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase )

        lowerCamelCase						:      Tuple							     = model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )
        lowerCamelCase						:      Tuple							     = model.get_config()
        # make sure that returned config is jsonifiable, which is required by keras
        json.dumps(__lowerCAmelCase )
        lowerCamelCase						:      List[Any]							     = model_class.from_config(model.get_config() )
        # make sure it also accepts a normal config
        lowerCamelCase						:      Union[str, Any]							     = model_class.from_config(model.config )
        lowerCamelCase						:      Dict							     = new_model(__lowerCAmelCase )  # Build model
        new_model.set_weights(model.get_weights() )
        lowerCamelCase						:      Any							     = new_model(__lowerCAmelCase       ,    noise=__lowerCAmelCase )

        self.assert_outputs_same(__lowerCAmelCase       ,    __lowerCAmelCase )
      @unittest.skip(
          reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.""" )
      def 		UpperCamelCase__				( self ):
       pass
      @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
      def 		UpperCamelCase__				( self ):
       pass






      @slow
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Any							     = TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""" )
       self.assertIsNotNone(__lowerCAmelCase )





def        _a   (		):
 lowerCamelCase						:      Tuple							     = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
 return image





@require_tf
@require_vision
class 				A__						(      unittest.TestCase):
      @cached_property
      def 		UpperCamelCase__				( self ):
       return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None






      @slow
      def 		UpperCamelCase__				( self ):
       np.random.seed(2 )

       lowerCamelCase						:      str							     = TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" )

       lowerCamelCase						:      Tuple							     = self.default_image_processor
       lowerCamelCase						:      Dict							     = prepare_img()
       lowerCamelCase						:      Dict							     = image_processor(images=__lowerCAmelCase       ,    return_tensors="""tf""" )

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       lowerCamelCase						:      Tuple							     = ViTMAEConfig()
       lowerCamelCase						:      Union[str, Any]							     = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
       lowerCamelCase						:      List[str]							     = np.random.uniform(size=(1, num_patches) )

       # forward pass
       lowerCamelCase						:      str							     = model(**__lowerCAmelCase       ,    noise=__lowerCAmelCase )

       # verify the logits
       lowerCamelCase						:      List[Any]							     = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
       self.assertEqual(outputs.logits.shape       ,    __lowerCAmelCase )

       lowerCamelCase						:      int							     = tf.convert_to_tensor(
           [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )

       tf.debugging.assert_near(outputs.logits[0, :3, :3]       ,    __lowerCAmelCase       ,    atol=1e-4 )












import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


SCREAMING_SNAKE_CASE__   :      List[Any]				=		os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"{bindir}/../../examples/pytorch/translation"):
					from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""sshleifer/student_marian_en_ro_6_1"""
SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""sshleifer/tiny-mbart"""
@require_torch
class     lowerCamelCase_					(   lowerCamelCase			):







				def       A						(    self							,       __lowerCAmelCase=False							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.run_trainer(
								    eval_steps=1							,       max_len=1_2							,       model_name=__lowerCAmelCase							,       num_train_epochs=1							,       distributed=__lowerCAmelCase							,       extra_args_str=__lowerCAmelCase							,       predict_with_generate=__lowerCAmelCase							,       do_train=__lowerCAmelCase							,       do_eval=__lowerCAmelCase							,       do_predict=__lowerCAmelCase							,       )
								__magic_name__		:Any   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history

								if not do_eval:
												return

								__magic_name__		:Any   =   [log for log in logs if '''eval_loss''' in log.keys()]

								__magic_name__		:str   =   eval_metrics[0]
								if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												__magic_name__		:Tuple   =   eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']							,       __lowerCAmelCase  )
												assert not math.isnan(float(last_step_stats['''eval_loss''']  )  ), "eval_loss must not be `nan`"







				@require_torch_non_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp simple'''  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp simple --fp16'''  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp zero_dp_2'''							,       predict_with_generate=__lowerCAmelCase  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(
								    distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,       predict_with_generate=__lowerCAmelCase  )







				@require_apex
				@require_torch_gpu
				def       A						(    self  ):
								"""simple docstring"""
								# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
								# program and it breaks other tests that run from the same pytest worker, therefore until this is
								# sorted out it must be run only in an external program, that is distributed=True in this
								# test and only under one or more gpus - if we want cpu will need to make a special test
								#
								# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
								# 2nd main() call it botches the future eval.
								#
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--fp16 --fp16_backend=apex'''  )
								# test 2nd time - was getting eval_loss': nan'
								# to reproduce the problem set distributed=False
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--fp16 --fp16_backend=apex'''  )







				@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
				@require_torch_multi_gpu
				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
								__magic_name__		:Any   =   {
								    # test with the default log_level - should be info and thus log info once
								    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
								    # test with low log_level and log_level_replica - should be noisy on all processes
								    # now the info string should appear twice on 2 processes
								    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
								    # test with high log_level and low log_level_replica
								    # now the info string should appear once only on the replica
								    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
								    # test with high log_level and log_level_replica - should be quiet on all processes
								    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
								}

								__magic_name__		:Optional[Any]   =   experiments[experiment_id]
								__magic_name__		:List[Any]   =   {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
								__magic_name__		:Optional[int]   =   '''Running training'''
								with CaptureStderr() as cl:
												self.run_seqaseq_quick(**__lowerCAmelCase							,       extra_args_str=data['''extra_args_str''']  )
								__magic_name__		:int   =   len(re.findall(__lowerCAmelCase							,       cl.err  )  )
								self.assertEqual(__lowerCAmelCase							,       data['''n_matches''']  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.run_trainer(
								    eval_steps=2							,       max_len=1_2_8							,       model_name=__lowerCAmelCase							,       learning_rate=3E-4							,       num_train_epochs=1_0							,       distributed=__lowerCAmelCase							,       )

								# Check metrics
								__magic_name__		:Optional[int]   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
								__magic_name__		:List[str]   =   [log for log in logs if '''eval_loss''' in log.keys()]
								__magic_name__		:Any   =   eval_metrics[0]
								__magic_name__		:int   =   eval_metrics[-1]

								assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
								assert isinstance(last_step_stats['''eval_bleu''']							,       __lowerCAmelCase  )

								# test if do_predict saves generations and metrics
								__magic_name__		:List[Any]   =   os.listdir(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   {os.path.basename(__lowerCAmelCase  ) for p in contents}
								assert "generated_predictions.txt" in contents
								assert "predict_results.json" in contents







				@slow
				@require_bitsandbytes
				def       A						(    self  ):
								"""simple docstring"""
								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(__lowerCAmelCase  ) -> Tuple[int, float]:
												__magic_name__		:str   =   '''--skip_memory_metrics 0'''

												__magic_name__		:Dict   =   self.run_trainer(
												    max_len=1_2_8							,       model_name=__lowerCAmelCase							,       learning_rate=3E-4							,       num_train_epochs=1							,       optim=__lowerCAmelCase							,       distributed=__lowerCAmelCase							,       extra_args_str=__lowerCAmelCase							,       do_eval=__lowerCAmelCase							,       do_predict=__lowerCAmelCase							,       n_gpus_to_use=1							,       )

												# Check metrics
												__magic_name__		:Optional[Any]   =   TrainerState.load_from_json(Path(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
												__magic_name__		:int   =   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0  )
												__magic_name__		:Optional[Any]   =   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0  )

												__magic_name__		:Any   =   logs[0]['''train_loss''']
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

								__magic_name__  ,					__magic_name__  ,					__magic_name__		:int   =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

								__magic_name__		:Tuple   =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								__magic_name__		:Tuple   =   gpu_peak_mem_orig + gpu_alloc_mem_orig
								__magic_name__		:List[Any]   =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								__magic_name__		:Optional[int]   =   gpu_total_mem_orig - gpu_total_mem_bnb

								# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
								# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
								# in 2 bytes and the diff in optim memory usage is derived as so:
								#
								# - normal 25*8=~200MB (8 bytes per param)
								# - bnb    25*2= ~50MB (2 bytes per param)
								#
								# Thus we should expect ~150MB total memory saved.
								#
								# Peak memory should be the same - the total should be different by about that same margin
								#
								# After leaving a small margin to accommodate for differences between gpus let's check
								# that we have at least 120MB in savings
								__magic_name__		:Optional[Any]   =   1_2_0

								# uncomment the following if this test starts failing - requires py38 for a new print feature
								# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
								# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
								# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
								# print(f"{gpu_alloc_mem_diff=}MB")
								# print(f"{gpu_peak_mem_diff=}MB")
								# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
								# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

								self.assertGreater(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
								    F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
								    F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,       )

								self.assertGreater(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
								    F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
								    F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,       )

								self.assertEqual(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 3E-3							,       __lowerCAmelCase = "adafactor"							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								__magic_name__		:Dict   =   self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple   =   F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCAmelCase  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCAmelCase  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

								__magic_name__		:str   =   F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCAmelCase  )}
        '''.split()

								__magic_name__		:Dict   =   '''
            --do_predict
        '''.split()

								__magic_name__		:Optional[int]   =   []
								if do_train:
												args += args_train

								if do_eval:
												args += args_eval

								if do_predict:
												args += args_predict

								if predict_with_generate:
												args += "--predict_with_generate".split()

								if do_train:
												if optim == "adafactor":
																args += "--adafactor".split()
												else:
																args += F'''--optim {optim}'''.split()

								if extra_args_str is not None:
												args += extra_args_str.split()

								if distributed:
												if n_gpus_to_use is None:
																__magic_name__		:List[Any]   =   get_gpu_count()
												__magic_name__		:Tuple   =   get_torch_dist_unique_port()
												__magic_name__		:Union[str, Any]   =   F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
												__magic_name__		:Any   =   [sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(__lowerCAmelCase							,       env=self.get_env()  )
								else:
												__magic_name__		:List[Any]   =   ['''run_translation.py'''] + args
												with patch.object(__lowerCAmelCase							,       '''argv'''							,       __lowerCAmelCase  ):
																main()

								return output_dir






