






"""simple docstring"""







import math

import flax.linen as nn
import jax.numpy as jnp





def 		lowercase__(A   ,						A   ,						A = 1   ,						A = 1   ,						A = 1.0e4   ,						A = False   ,						A = 1.0   ,						)							->jnp.ndarray:




						"""simple docstring"""



						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase__							: Optional[int]=			float(embedding_dim // 2		)
						lowercase__							: Optional[Any]=			math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
						lowercase__							: Union[str, Any]=			min_timescale * jnp.exp(jnp.arange(A   ,						dtype=jnp.floataa		) * -log_timescale_increment		)
						lowercase__							: int=			jnp.expand_dims(A   ,						1		) * jnp.expand_dims(A   ,						0		)

						# scale embeddings
						lowercase__							: Optional[int]=			scale * emb

						if flip_sin_to_cos:
												lowercase__							: Union[str, Any]=			jnp.concatenate([jnp.cos(A		), jnp.sin(A		)]   ,						axis=1		)
						else:
												lowercase__							: List[Any]=			jnp.concatenate([jnp.sin(A		), jnp.cos(A		)]   ,						axis=1		)
						lowercase__							: Optional[Any]=			jnp.reshape(A   ,						[jnp.shape(A		)[0], embedding_dim]		)
						return signal


class        __UpperCAmelCase(  nn.Module				):




						"""simple docstring"""




						__lowerCamelCase										=   32
						__lowerCamelCase										=   jnp.floataa




						@nn.compact
						def __call__(  self			,    snake_case__     ):
												'''simple docstring'''
												lowercase__							: Optional[Any]=			nn.Dense(self.time_embed_dim			,    dtype=self.dtype			,    name="linear_1"     )(snake_case__     )
												lowercase__							: str=			nn.silu(snake_case__     )
												lowercase__							: str=			nn.Dense(self.time_embed_dim			,    dtype=self.dtype			,    name="linear_2"     )(snake_case__     )
												return temb


class        __UpperCAmelCase(  nn.Module				):




						"""simple docstring"""




						__lowerCamelCase										=   32
						__lowerCamelCase										=   False
						__lowerCamelCase										=   1




						@nn.compact
						def __call__(  self			,    snake_case__     ):
												'''simple docstring'''
												return get_sinusoidal_embeddings(
												    snake_case__			,    embedding_dim=self.dim			,    flip_sin_to_cos=self.flip_sin_to_cos			,    freq_shift=self.freq_shift     )








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     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :int   ,     lowercase      :Optional[Any]   ,     lowercase      :Optional[int]=1_3   ,     lowercase      :Any=7   ,     lowercase      :Tuple=True   ,     lowercase      :Optional[int]=True   ,     lowercase      :Any=False   ,     lowercase      :Any=True   ,     lowercase      :Dict=9_9   ,     lowercase      :Dict=3_2   ,     lowercase      :Any=5   ,     lowercase      :Optional[Any]=4   ,     lowercase      :List[str]=6_4   ,     lowercase      :Optional[int]="gelu"   ,     lowercase      :int=0.1   ,     lowercase      :str=0.1   ,     lowercase      :List[str]=5_1_2   ,     lowercase      :int=1_6   ,     lowercase      :Any=2   ,     lowercase      :Union[str, Any]=0.02   ,     lowercase      :Optional[int]=3   ,     lowercase      :Optional[Any]=4   ,     lowercase      :Tuple=None   ,     lowercase      :int=2   ,     lowercase      :Tuple=2   ,     lowercase      :List[Any]=2   ,     lowercase      :Optional[int]=2   ,     lowercase      :Tuple=4   ,     lowercase      :int=1   ,     )		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






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

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

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

				SCREAMING_SNAKE_CASE     						= self.get_config()

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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""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  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :Any   ,     lowercase      :List[str]   ,     lowercase      :Optional[Any]   ,     lowercase      :List[str]			)		->      str:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Dict   ,     lowercase      :Dict   ,     lowercase      :List[Any]   ,     lowercase      :str   ,     lowercase      :Union[str, Any]   ,     lowercase      :Dict   ,     lowercase      :List[Any]			)		->      str:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]   ,     lowercase      :Optional[Any]   ,     lowercase      :Optional[int]   ,     lowercase      :str   ,     lowercase      :int   ,     lowercase      :Optional[Any]   ,     lowercase      :int			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= SqueezeBertForQuestionAnswering(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(
				    lowercase   ,     attention_mask=lowercase   ,     start_positions=lowercase   ,     end_positions=lowercase			)
				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      :Any   ,     lowercase      :Optional[Any]   ,     lowercase      :List[str]   ,     lowercase      :int   ,     lowercase      :Any   ,     lowercase      :Optional[int]   ,     lowercase      :List[str]			)		->      Any:







				"""simple docstring"""






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







	def  snake_case__     (     self      :str   ,     lowercase      :List[Any]   ,     lowercase      :List[str]   ,     lowercase      :Optional[int]   ,     lowercase      :Tuple   ,     lowercase      :Tuple   ,     lowercase      :str			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.num_labels
				SCREAMING_SNAKE_CASE     						= SqueezeBertForTokenClassification(config=lowercase			)
				model.to(lowercase			)
				model.eval()

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







	def  snake_case__     (     self      :int   ,     lowercase      :List[str]   ,     lowercase      :List[Any]   ,     lowercase      :Tuple   ,     lowercase      :str   ,     lowercase      :Optional[Any]   ,     lowercase      :Optional[int]			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Union[str, Any]:







				"""simple docstring"""






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





@require_torch
class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					int    		=	(
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	UpperCamelCase_  :					int    		=	(
	    {
	        'feature-extraction': SqueezeBertModel,
	        'fill-mask': SqueezeBertForMaskedLM,
	        'question-answering': SqueezeBertForQuestionAnswering,
	        'text-classification': SqueezeBertForSequenceClassification,
	        'token-classification': SqueezeBertForTokenClassification,
	        'zero-shot': SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCamelCase_  :					Tuple    		=	False
	UpperCamelCase_  :					int    		=	True
	UpperCamelCase_  :					List[Any]    		=	False







	def  snake_case__     (     self      :Tuple			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= SqueezeBertModelTester(self			)
				SCREAMING_SNAKE_CASE     						= ConfigTester(self   ,     config_class=lowercase   ,     dim=3_7			)







	def  snake_case__     (     self      :Optional[Any]			)		->      Optional[int]:







				"""simple docstring"""






				self.config_tester.run_common_tests()







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_model(*lowercase			)







	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase			)







	def  snake_case__     (     self      :List[str]			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase			)







	def  snake_case__     (     self      :Any			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase			)







	@slow
	def  snake_case__     (     self      :Dict			)		->      str:







				"""simple docstring"""






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





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







	@slow
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''			)

				SCREAMING_SNAKE_CASE     						= torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]			)
				SCREAMING_SNAKE_CASE     						= model(lowercase			)[0]
				SCREAMING_SNAKE_CASE     						= torch.Size((1, 3)			)
				self.assertEqual(output.shape   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= torch.tensor([[0.64_01, -0.03_49, -0.60_41]]			)
				self.assertTrue(torch.allclose(lowercase   ,     lowercase   ,     atol=1e-4			)			)