

"""simple docstring"""


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a					:int    = """true"""




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=82  ,					__lowerCAmelCase=16							)       ->				Tuple:
      set_seed(42							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	RegressionModel()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	deepcopy(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	RegressionDataset(length=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	DataLoader(__lowerCAmelCase  ,					batch_size=__lowerCAmelCase							)
      model.to(accelerator.device							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	accelerator.prepare(__lowerCAmelCase  ,					__lowerCAmelCase							)
      return model, ddp_model, dataloader




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      str      		=	AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""							)
      SCREAMING_SNAKE_CASE__   :      str      		=	load_dataset("""glue"""  ,					"""mrpc"""  ,					split="""validation"""							)

      def tokenize_function(__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer(examples["""sentence1"""]  ,					examples["""sentence2"""]  ,					truncation=__lowerCAmelCase  ,					max_length=__lowerCAmelCase							)
            return outputs

      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	dataset.map(
                __lowerCAmelCase  ,					batched=__lowerCAmelCase  ,					remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,					)

      SCREAMING_SNAKE_CASE__   :      str      		=	tokenized_datasets.rename_column("""label"""  ,					"""labels"""							)

      def collate_fn(__lowerCAmelCase							):
            if use_longest:
                  return tokenizer.pad(__lowerCAmelCase  ,					padding="""longest"""  ,					return_tensors="""pt"""							)
            return tokenizer.pad(__lowerCAmelCase  ,					padding="""max_length"""  ,					max_length=128  ,					return_tensors="""pt"""							)

      return DataLoader(__lowerCAmelCase  ,					shuffle=__lowerCAmelCase  ,					collate_fn=__lowerCAmelCase  ,					batch_size=16							)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Accelerator(dispatch_batches=__lowerCAmelCase  ,					split_batches=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	get_dataloader(__lowerCAmelCase  ,					not dispatch_batches							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	AutoModelForSequenceClassification.from_pretrained(
          """hf-internal-testing/mrpc-bert-base-cased"""  ,					return_dict=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	accelerator.prepare(__lowerCAmelCase  ,					__lowerCAmelCase							)
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[]
      for batch in dataloader:
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	batch.values()
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE__   :      int      		=	model(__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	accelerator.gather_for_metrics((logit, target)							)
                  logits_and_targets.append((logit, target)							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	[], []
      for logit, targ in logits_and_targets:
            logits.append(__lowerCAmelCase							)
            targs.append(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.cat(__lowerCAmelCase							), torch.cat(__lowerCAmelCase							)
      return logits, targs




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=82  ,					__lowerCAmelCase=False  ,					__lowerCAmelCase=False  ,					__lowerCAmelCase=16							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	get_basic_setup(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	generate_predictions(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
      assert (
          len(__lowerCAmelCase							) == num_samples
      ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCAmelCase							)}'''




def  _lowercase					(    __lowerCAmelCase = False  ,					__lowerCAmelCase = False							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	evaluate.load("""glue"""  ,					"""mrpc"""							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[Any]      		=	get_mrpc_setup(__lowerCAmelCase  ,					__lowerCAmelCase							)
      # First do baseline
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	setup["""no"""]
      model.to(__lowerCAmelCase							)
      model.eval()
      for batch in dataloader:
            batch.to(__lowerCAmelCase							)
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(**__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	outputs.logits.argmax(dim=-1							)
            metric.add_batch(predictions=__lowerCAmelCase  ,					references=batch["""labels"""]							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	metric.compute()

      # Then do distributed
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	setup["""ddp"""]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE__   :      Any      		=	model(**__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE__   :      int      		=	batch["""labels"""]
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	accelerator.gather_for_metrics((preds, references)							)
            metric.add_batch(predictions=__lowerCAmelCase  ,					references=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]  ,					distributed[key]							), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''




def  _lowercase					(    )       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      int      		=	Accelerator(split_batches=__lowerCAmelCase  ,					dispatch_batches=__lowerCAmelCase							)
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print("""**Testing gather_for_metrics**"""							)
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''							)
                        test_mrpc(__lowerCAmelCase  ,					__lowerCAmelCase							)
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print("""**Test torch metrics**"""							)
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	Accelerator(split_batches=__lowerCAmelCase  ,					dispatch_batches=__lowerCAmelCase							)
                  if accelerator.is_local_main_process:
                        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''							)
                  test_torch_metrics(__lowerCAmelCase  ,					99							)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print("""**Test last batch is not dropped when perfectly divisible**"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Accelerator()
      test_torch_metrics(__lowerCAmelCase  ,					512							)
      accelerator.state._reset_state()




def  _lowercase					(    __lowerCAmelCase							)       ->				Tuple:
      main()


if __name__ == "__main__":
  main()







"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_token_type_ids
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      int      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	type_vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_choices

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_attention_mask:
                 SCREAMING_SNAKE_CASE__   :      int      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	RoFormerConfig(
               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	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    )

           return config, input_ids, token_type_ids, attention_mask





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]  )

           self.assertTrue(jnp.allclose(output[:, :3, :3]	,    _a	,    atol=1E-4  )  )




