


'''simple docstring'''






import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		)




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=14   ,       lowerCamelCase_=7   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=99   ,       lowerCamelCase_=32   ,       lowerCamelCase_=5   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_12   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=None   ,       )			->				Optional[Any]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      seq_length
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_token_type_ids
														lowerCAmelCase__			  =      use_input_mask
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      use_mc_token_ids
														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__			  =      num_labels
														lowerCAmelCase__			  =      num_choices
														lowerCAmelCase__			  =      scope
														lowerCAmelCase__			  =      self.vocab_size - 1







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size  )

														lowerCAmelCase__			  =      None
														if self.use_input_mask:
																					lowerCAmelCase__			  =      random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCAmelCase__			  =      None
														if self.use_token_type_ids:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size  )

														lowerCAmelCase__			  =      None
														if self.use_mc_token_ids:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.num_choices]   ,       self.seq_length  )

														lowerCAmelCase__			  =      None
														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.seq_length]   ,       self.num_labels  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.num_choices  )

														lowerCAmelCase__			  =      self.get_config()

														lowerCAmelCase__			  =      ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,       2  )

														return (
														    config,
														    input_ids,
														    input_mask,
														    head_mask,
														    token_type_ids,
														    mc_token_ids,
														    sequence_labels,
														    token_labels,
														    choice_labels,
														)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return CTRLConfig(
														    vocab_size=self.vocab_size   ,       n_embd=self.hidden_size   ,       n_layer=self.num_hidden_layers   ,       n_head=self.num_attention_heads   ,       n_positions=self.max_position_embeddings   ,       pad_token_id=self.pad_token_id   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__			  =      CTRLModel(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()

														model(lowerCamelCase_   ,       token_type_ids=lowerCamelCase_   ,       head_mask=lowerCamelCase_  )
														model(lowerCamelCase_   ,       token_type_ids=lowerCamelCase_  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)  )
														self.parent.assertEqual(len(result.past_key_values  )   ,       config.n_layer  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				Tuple:
														lowerCAmelCase__			  =      CTRLLMHeadModel(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()

														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       token_type_ids=lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.loss.shape   ,       ()  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()

														(
														    (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   
														)			  =      config_and_inputs

														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

														return config, inputs_dict







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      CTRLForSequenceClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.type_sequence_label_size  )
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       token_type_ids=lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )




@require_torch
class 						a__							(				a__							,							a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Tuple      		= (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowercase__     :  Any      		= (CTRLLMHeadModel,) if is_torch_available() else ()
							lowercase__     :  Dict      		= (
							    {
							        "feature-extraction": CTRLModel,
							        "text-classification": CTRLForSequenceClassification,
							        "text-generation": CTRLLMHeadModel,
							        "zero-shot": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase__     :  int      		= True
							lowercase__     :  Optional[Any]      		= False
							lowercase__     :  str      		= False







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																					# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																					# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																					# config could not be created.
																					return True

														return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      CTRLModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       n_embd=37  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														super().tearDown()
														# clean-up as much as possible GPU memory occupied by PyTorch
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														self.config_tester.run_common_tests()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_ctrl_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head_model(*lowerCamelCase_  )







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														pass







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      CTRLModel.from_pretrained(lowerCamelCase_  )
																					self.assertIsNotNone(lowerCamelCase_  )







							@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														pass




@require_torch
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														super().tearDown()
														# clean-up as much as possible GPU memory occupied by PyTorch
														gc.collect()
														torch.cuda.empty_cache()







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      CTRLLMHeadModel.from_pretrained('''ctrl'''  )
														model.to(lowerCamelCase_  )
														lowerCAmelCase__			  =      torch.tensor(
														    [[1_18_59, 0, 16_11, 8]]   ,       dtype=torch.long   ,       device=lowerCamelCase_  )  # Legal the president is
														lowerCAmelCase__			  =      [
														    1_18_59,
														    0,
														    16_11,
														    8,
														    5,
														    1_50,
														    2_64_49,
														    2,
														    19,
														    3_48,
														    4_69,
														    3,
														    25_95,
														    48,
														    2_07_40,
														    24_65_33,
														    24_65_33,
														    19,
														    30,
														    5,
														]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

														lowerCAmelCase__			  =      model.generate(lowerCamelCase_   ,       do_sample=lowerCamelCase_  )
														self.assertListEqual(output_ids[0].tolist()   ,       lowerCamelCase_  )



'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														lowerCAmelCase__			  =      length
														lowerCAmelCase__			  =      rng.normal(size=(length,)  ).astype(np.floataa  )
														lowerCAmelCase__			  =      a * self.x + b + rng.normal(scale=0.1   ,       size=(length,)  ).astype(np.floataa  )







							def __len__(						self  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a[0] + self.b[0]




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a + self.b


def    _snake_case  (	A					,       A = 16			)		->       Any:
							from datasets import load_dataset
							from transformers import AutoTokenizer

							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							lowerCAmelCase__			  =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
							lowerCAmelCase__			  =      load_dataset('''csv'''					,       data_files=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

							lowerCAmelCase__			  =      {v: i for i, v in enumerate(A			)}

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       padding='''max_length'''			)
														if "label" in examples:
																					lowerCAmelCase__			  =      [label_to_id[l] for l in examples['''label''']]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCAmelCase__			  =      datasets.map(
							    A					,       batched=A					,       remove_columns=['''sentence1''', '''sentence2''', '''label''']					,       )

							def collate_fn(A			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A					,       padding='''max_length'''					,       max_length=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

							# Instantiate dataloaders.
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''train''']					,       shuffle=A					,       collate_fn=A					,       batch_size=2			)
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''validation''']					,       shuffle=A					,       collate_fn=A					,       batch_size=1			)

							return train_dataloader, eval_dataloader