




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

from accelerate.utils.dataclasses import DistributedType





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=2  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=3  ,      SCREAMING_SNAKE_CASE__	:	Tuple=64  ,      SCREAMING_SNAKE_CASE__	:	Tuple=None  ) ->			int:
         lowerCAmelCase__            =					np.random.default_rng(SCREAMING_SNAKE_CASE__  )
         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	:	Dict  ) ->			List[str]:
         return self.length


    def __getitem__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Optional[int]:
         return {"x": self.x[i], "y": self.y[i]}






class 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

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


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=None  ) ->			Tuple:
         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 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=False  ) ->			Dict:
         super().__init__()
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					True


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ) ->			Dict:
         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 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   int = 16     ):
     """simple docstring"""

     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=lowerCAmelCase_     )
     lowerCAmelCase__            =					datasets["train"].unique("label"     )

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

     def tokenize_function(lowerCAmelCase_	:   Optional[Any]     ):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase__            =					tokenizer(
              examples["sentence1"]  ,							examples["sentence2"]  ,							truncation=lowerCAmelCase_  ,							max_length=lowerCAmelCase_  ,							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(
         lowerCAmelCase_  ,							batched=lowerCAmelCase_  ,							remove_columns=["sentence1", "sentence2", "label"]  ,							)

     def collate_fn(lowerCAmelCase_	:   Dict     ):
          # 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(lowerCAmelCase_  ,							padding="max_length"  ,							max_length=128  ,							return_tensors="pt"     )
          return tokenizer.pad(lowerCAmelCase_  ,							padding="longest"  ,							return_tensors="pt"     )

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

     return train_dataloader, eval_dataloader








import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[str]=13			,						UpperCamelCase_							:      int=7			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      str=False			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      Union[str, Any]=99			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      Union[str, Any]=5			,						UpperCamelCase_							:      List[str]=4			,						UpperCamelCase_							:      Union[str, Any]=64			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      int=512			,						UpperCamelCase_							:      Dict=16			,						UpperCamelCase_							:      List[str]=2			,						UpperCamelCase_							:      int=0.02			,						UpperCamelCase_							:      Dict=3			,						UpperCamelCase_							:      List[Any]=4			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      Optional[Any]=2			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      List[str]=1			,						):


         """simple docstring"""



         __A       =						parent
         __A       =						batch_size
         __A       =						seq_length
         __A       =						is_training
         __A       =						use_input_mask
         __A       =						use_token_type_ids
         __A       =						use_labels
         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						type_sequence_label_size
         __A       =						initializer_range
         __A       =						num_labels
         __A       =						num_choices
         __A       =						scope
         __A       =						q_groups
         __A       =						k_groups
         __A       =						v_groups
         __A       =						post_attention_groups
         __A       =						intermediate_groups
         __A       =						output_groups




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size					)

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

         __A       =						None
         __A       =						None
         __A       =						None
         if self.use_labels:
             __A       =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size					)
             __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels					)
             __A       =						ids_tensor([self.batch_size]			,						self.num_choices					)

         __A       =						self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return SqueezeBertConfig(
             embedding_size=self.hidden_size			,						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			,						attention_probs_dropout_prob=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						q_groups=self.q_groups			,						k_groups=self.k_groups			,						v_groups=self.v_groups			,						post_attention_groups=self.post_attention_groups			,						intermediate_groups=self.intermediate_groups			,						output_groups=self.output_groups			,						)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						SqueezeBertModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForMaskedLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						SqueezeBertForQuestionAnswering(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForTokenClassification(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()

         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						self.num_choices
         __A       =						SqueezeBertForMultipleChoice(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						input_ids.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						input_mask.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_			,						)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices)					)





     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						self.prepare_config_and_inputs()
         ((__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A))       =						config_and_inputs
         __A       =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE     =					(
         {
             "feature-extraction": SqueezeBertModel,
             "fill-mask": SqueezeBertForMaskedLM,
             "question-answering": SqueezeBertForQuestionAnswering,
             "text-classification": SqueezeBertForSequenceClassification,
             "token-classification": SqueezeBertForTokenClassification,
             "zero-shot": SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						SqueezeBertModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						dim=37					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A       =						SqueezeBertModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)


@require_sentencepiece
@require_tokenizers
@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""					)

         __A       =						torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]					)
         __A       =						model(UpperCamelCase_					)[0]
         __A       =						torch.Size((1, 3)					)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         __A       =						torch.tensor([[0.6401, -0.0349, -0.6041]]					)
         self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-4					)					)




