


'''simple docstring'''

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         LayoutLMvaForQuestionAnswering,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaModel,
     )
     from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=2			,  UpperCamelCase=3			,  UpperCamelCase=4			,  UpperCamelCase=2			,  UpperCamelCase=7			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=99			,  UpperCamelCase=36			,  UpperCamelCase=3			,  UpperCamelCase=4			,  UpperCamelCase=37			,  UpperCamelCase="gelu"			,  UpperCamelCase=0.1			,  UpperCamelCase=0.1			,  UpperCamelCase=512			,  UpperCamelCase=16			,  UpperCamelCase=2			,  UpperCamelCase=0.02			,  UpperCamelCase=6			,  UpperCamelCase=6			,  UpperCamelCase=3			,  UpperCamelCase=4			,  UpperCamelCase=None			,  UpperCamelCase=1000			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  patch_size
   lowerCamelCase_      =  text_seq_length
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_input_mask
   lowerCamelCase_      =  use_token_type_ids
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  vocab_size
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  max_position_embeddings
   lowerCamelCase_      =  type_vocab_size
   lowerCamelCase_      =  type_sequence_label_size
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  coordinate_size
   lowerCamelCase_      =  shape_size
   lowerCamelCase_      =  num_labels
   lowerCamelCase_      =  num_choices
   lowerCamelCase_      =  scope
   lowerCamelCase_      =  range_bbox

   # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
   lowerCamelCase_      =  text_seq_length
   lowerCamelCase_      =  (image_size // patch_size) ** 2 + 1
   lowerCamelCase_      =  self.text_seq_length + self.image_seq_length






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.text_seq_length]			,  self.vocab_size     )

   lowerCamelCase_      =  ids_tensor([self.batch_size, self.text_seq_length, 4]			,  self.range_bbox     )
   # Ensure that bbox is legal
   for i in range(bbox.shape[0]     ):
    for j in range(bbox.shape[1]     ):
     if bbox[i, j, 3] < bbox[i, j, 1]:
      lowerCamelCase_      =  bbox[i, j, 3]
      lowerCamelCase_      =  bbox[i, j, 1]
      lowerCamelCase_      =  t
     if bbox[i, j, 2] < bbox[i, j, 0]:
      lowerCamelCase_      =  bbox[i, j, 2]
      lowerCamelCase_      =  bbox[i, j, 0]
      lowerCamelCase_      =  t

   lowerCamelCase_      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

   lowerCamelCase_      =  None
   if self.use_input_mask:
    lowerCamelCase_      =  random_attention_mask([self.batch_size, self.text_seq_length]     )

   lowerCamelCase_      =  None
   if self.use_token_type_ids:
    lowerCamelCase_      =  ids_tensor([self.batch_size, self.text_seq_length]			,  self.type_vocab_size     )

   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   if self.use_labels:
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.type_sequence_label_size     )
    lowerCamelCase_      =  ids_tensor([self.batch_size, self.text_seq_length]			,  self.num_labels     )

   lowerCamelCase_      =  LayoutLMvaConfig(
       vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  num_attention_heads=self.num_attention_heads			,  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			,  max_position_embeddings=self.max_position_embeddings			,  type_vocab_size=self.type_vocab_size			,  initializer_range=self.initializer_range			,  coordinate_size=self.coordinate_size			,  shape_size=self.shape_size			,  input_size=self.image_size			,  patch_size=self.patch_size			,  )

   return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  LayoutLMvaModel(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()

   # text + image
   lowerCamelCase_      =  model(UpperCamelCase			,  pixel_values=UpperCamelCase     )
   lowerCamelCase_      =  model(
       UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase     )

   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )

   # text only
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(
       result.last_hidden_state.shape			,  (self.batch_size, self.text_seq_length, self.hidden_size)     )

   # image only
   lowerCamelCase_      =  model(pixel_values=UpperCamelCase     )
   self.parent.assertEqual(
       result.last_hidden_state.shape			,  (self.batch_size, self.image_seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  LayoutLMvaForSequenceClassification(UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(
       UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase			,  labels=UpperCamelCase			,  )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  LayoutLMvaForTokenClassification(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(
       UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase			,  labels=UpperCamelCase			,  )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.text_seq_length, self.num_labels)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  LayoutLMvaForQuestionAnswering(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(
       UpperCamelCase			,  bbox=UpperCamelCase			,  pixel_values=UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase			,  start_positions=UpperCamelCase			,  end_positions=UpperCamelCase			,  )
   self.parent.assertEqual(result.start_logits.shape			,  (self.batch_size, self.seq_length)     )
   self.parent.assertEqual(result.end_logits.shape			,  (self.batch_size, self.seq_length)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  config_and_inputs
   lowerCamelCase_      =  {
       "input_ids": input_ids,
       "bbox": bbox,
       "pixel_values": pixel_values,
       "token_type_ids": token_type_ids,
       "attention_mask": input_mask,
   }
   return config, inputs_dict





@require_torch
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False

  _lowerCamelCase		    =  (
      (
          LayoutLMvaModel,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
      if is_torch_available()
      else {}
  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
   # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
   # the sequence dimension of the text embedding only.
   # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
   return True






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  LayoutLMvaModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_      =  copy.deepcopy(UpperCamelCase     )
   if model_class in get_values(UpperCamelCase     ):
    lowerCamelCase_      =  {
        k: v.unsqueeze(1     ).expand(-1			,  self.model_tester.num_choices			,  -1     ).contiguous()
        if isinstance(UpperCamelCase			,  torch.Tensor     ) and v.ndim > 1
        else v
        for k, v in inputs_dict.items()
    }
   if return_labels:
    if model_class in get_values(UpperCamelCase     ):
     lowerCamelCase_      =  torch.ones(self.model_tester.batch_size			,  dtype=torch.long			,  device=UpperCamelCase     )
    elif model_class in get_values(UpperCamelCase     ):
     lowerCamelCase_      =  torch.zeros(
         self.model_tester.batch_size			,  dtype=torch.long			,  device=UpperCamelCase     )
     lowerCamelCase_      =  torch.zeros(
         self.model_tester.batch_size			,  dtype=torch.long			,  device=UpperCamelCase     )
    elif model_class in [
        *get_values(UpperCamelCase     ),
    ]:
     lowerCamelCase_      =  torch.zeros(
         self.model_tester.batch_size			,  dtype=torch.long			,  device=UpperCamelCase     )
    elif model_class in [
        *get_values(UpperCamelCase     ),
    ]:
     lowerCamelCase_      =  torch.zeros(
         (self.model_tester.batch_size, self.model_tester.text_seq_length)			,  dtype=torch.long			,  device=UpperCamelCase			,  )

   return inputs_dict






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    lowerCamelCase_      =  type
    self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*UpperCamelCase     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  LayoutLMvaModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )





def __snake_case					(      ):
 lowerCamelCase_      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image







@require_torch
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase     ) if is_vision_available() else None







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"     ).to(UpperCamelCase     )

   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="pt"     ).pixel_values.to(UpperCamelCase     )

   lowerCamelCase_      =  torch.tensor([[1, 2]]     )
   lowerCamelCase_      =  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

   # forward pass
   lowerCamelCase_      =  model(
       input_ids=input_ids.to(UpperCamelCase     )			,  bbox=bbox.to(UpperCamelCase     )			,  pixel_values=pixel_values.to(UpperCamelCase     )			,  )

   # verify the logits
   lowerCamelCase_      =  torch.Size((1, 199, 768)     )
   self.assertEqual(outputs.last_hidden_state.shape			,  UpperCamelCase     )

   lowerCamelCase_      =  torch.tensor(
       [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]     ).to(UpperCamelCase     )

   self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,  UpperCamelCase			,  atol=1e-4     )     )







'''simple docstring'''

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


a_  :				Dict						= os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
     from run_translation import main  # noqa


set_seed(42)
a_  :				int						= """sshleifer/student_marian_en_ro_6_1"""
a_  :				str						= """sshleifer/tiny-mbart"""





@require_torch
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase=False			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=1			,  max_len=12			,  model_name=UpperCamelCase			,  num_train_epochs=1			,  distributed=UpperCamelCase			,  extra_args_str=UpperCamelCase			,  predict_with_generate=UpperCamelCase			,  do_train=UpperCamelCase			,  do_eval=UpperCamelCase			,  do_predict=UpperCamelCase			,  )
   lowerCamelCase_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history

   if not do_eval:
    return

   lowerCamelCase_      =  [log for log in logs if "eval_loss" in log.keys()]

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

    lowerCamelCase_      =  eval_metrics[-1]
    assert isinstance(last_step_stats["eval_bleu"]			,  UpperCamelCase     )
    assert not math.isnan(float(last_step_stats["eval_loss"]     )     ), "eval_loss must not be `nan`"






  @require_torch_non_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp simple"     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  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   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2"			,  predict_with_generate=UpperCamelCase     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(
       distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2 --fp16"			,  predict_with_generate=UpperCamelCase     )






  @require_apex
  @require_torch_gpu
  def   snake_case		(    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=UpperCamelCase			,  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=UpperCamelCase			,  extra_args_str="--fp16 --fp16_backend=apex"     )






  @parameterized.expand(["base", "low", "high", "mixed"]     )
  @require_torch_multi_gpu
  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
   lowerCamelCase_      =  {
       # 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},
   }

   lowerCamelCase_      =  experiments[experiment_id]
   lowerCamelCase_      =  {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
   lowerCamelCase_      =  "Running training"
   with CaptureStderr() as cl:
    self.run_seqaseq_quick(**UpperCamelCase			,  extra_args_str=data["extra_args_str"]     )
   lowerCamelCase_      =  len(re.findall(UpperCamelCase			,  cl.err     )     )
   self.assertEqual(UpperCamelCase			,  data["n_matches"]     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=2			,  max_len=128			,  model_name=UpperCamelCase			,  learning_rate=3e-4			,  num_train_epochs=10			,  distributed=UpperCamelCase			,  )

   # Check metrics
   lowerCamelCase_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history
   lowerCamelCase_      =  [log for log in logs if "eval_loss" in log.keys()]
   lowerCamelCase_      =  eval_metrics[0]
   lowerCamelCase_      =  eval_metrics[-1]

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

   # test if do_predict saves generations and metrics
   lowerCamelCase_      =  os.listdir(UpperCamelCase     )
   lowerCamelCase_      =  {os.path.basename(UpperCamelCase     ) for p in contents}
   assert "generated_predictions.txt" in contents
   assert "predict_results.json" in contents






  @slow
  @require_bitsandbytes
  def   snake_case		(    self     ):







   """simple docstring"""
   from transformers.training_args import OptimizerNames

   def train_and_return_metrics(UpperCamelCase     ) -> Tuple[int, float]:
    lowerCamelCase_      =  "--skip_memory_metrics 0"

    lowerCamelCase_      =  self.run_trainer(
        max_len=128			,  model_name=UpperCamelCase			,  learning_rate=3e-4			,  num_train_epochs=1			,  optim=UpperCamelCase			,  distributed=UpperCamelCase			,  extra_args_str=UpperCamelCase			,  do_eval=UpperCamelCase			,  do_predict=UpperCamelCase			,  n_gpus_to_use=1			,  )

    # Check metrics
    lowerCamelCase_      =  TrainerState.load_from_json(Path(UpperCamelCase			,  "trainer_state.json"     )     ).log_history
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20     )
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20     )

    lowerCamelCase_      =  logs[0]["train_loss"]
    return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value     )
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  train_and_return_metrics(OptimizerNames.ADAMW_BNB.value     )

   lowerCamelCase_      =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

   lowerCamelCase_      =  gpu_peak_mem_orig + gpu_alloc_mem_orig
   lowerCamelCase_      =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
   lowerCamelCase_      =  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
   lowerCamelCase_      =  120

   # 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(
       UpperCamelCase			,  UpperCamelCase			,  "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(
       UpperCamelCase			,  UpperCamelCase			,  "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(
       UpperCamelCase			,  UpperCamelCase			,  f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''     )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = 3e-3			,  UpperCamelCase = "adafactor"			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = 0			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
   lowerCamelCase_      =  self.get_auto_remove_tmp_dir()
   lowerCamelCase_      =  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(UpperCamelCase     )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase     )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

   lowerCamelCase_      =  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(UpperCamelCase     )}
        '''.split()

   lowerCamelCase_      =  "\n            --do_predict\n        ".split()

   lowerCamelCase_      =  []
   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:
     lowerCamelCase_      =  get_gpu_count()
    lowerCamelCase_      =  get_torch_dist_unique_port()
    lowerCamelCase_      =  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()
    lowerCamelCase_      =  [sys.executable] + distributed_args + args
    # keep for quick debug
    # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
    execute_subprocess_async(UpperCamelCase			,  env=self.get_env()     )
   else:
    lowerCamelCase_      =  ["run_translation.py"] + args
    with patch.object(UpperCamelCase			,  "argv"			,  UpperCamelCase     ):
     main()

   return output_dir



