



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   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=True     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=99     ,lowercase=32     ,lowercase=5     ,lowercase=4     ,lowercase=64     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=16     ,lowercase=2     ,lowercase=0.02     ,lowercase=3     ,lowercase=4     ,lowercase=None     ,lowercase=2     ,lowercase=2     ,lowercase=2     ,lowercase=2     ,lowercase=4     ,lowercase=1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		parent
             UpperCAmelCase_  :	Dict				      =		batch_size
             UpperCAmelCase_  :	Tuple				      =		seq_length
             UpperCAmelCase_  :	Dict				      =		is_training
             UpperCAmelCase_  :	str				      =		use_input_mask
             UpperCAmelCase_  :	Union[str, Any]				      =		use_token_type_ids
             UpperCAmelCase_  :	Union[str, Any]				      =		use_labels
             UpperCAmelCase_  :	int				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	Tuple				      =		num_hidden_layers
             UpperCAmelCase_  :	Dict				      =		num_attention_heads
             UpperCAmelCase_  :	int				      =		intermediate_size
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	Optional[int]				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Tuple				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Dict				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	int				      =		type_sequence_label_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	List[Any]				      =		num_labels
             UpperCAmelCase_  :	List[str]				      =		num_choices
             UpperCAmelCase_  :	Optional[int]				      =		scope
             UpperCAmelCase_  :	Union[str, Any]				      =		q_groups
             UpperCAmelCase_  :	Optional[Any]				      =		k_groups
             UpperCAmelCase_  :	List[Any]				      =		v_groups
             UpperCAmelCase_  :	Optional[Any]				      =		post_attention_groups
             UpperCAmelCase_  :	Optional[Any]				      =		intermediate_groups
             UpperCAmelCase_  :	str				      =		output_groups



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

             UpperCAmelCase_  :	Union[str, Any]				      =		None
             UpperCAmelCase_  :	List[str]				      =		None
             UpperCAmelCase_  :	Optional[Any]				      =		None
             if self.use_labels:
                   UpperCAmelCase_  :	Optional[Any]				      =		ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
                   UpperCAmelCase_  :	str				      =		ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels)
                   UpperCAmelCase_  :	Dict				      =		ids_tensor([self.batch_size]     ,self.num_choices)

             UpperCAmelCase_  :	Optional[Any]				      =		self.get_config()

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



       def  A_   (     self):

             """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  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		SqueezeBertModel(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Union[str, Any]				      =		model(lowercase     ,lowercase)
             UpperCAmelCase_  :	str				      =		model(lowercase)
             self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		SqueezeBertForMaskedLM(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Any				      =		model(lowercase     ,attention_mask=lowercase     ,labels=lowercase)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		SqueezeBertForQuestionAnswering(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Dict				      =		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  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.num_labels
             UpperCAmelCase_  :	int				      =		SqueezeBertForSequenceClassification(lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Dict				      =		model(lowercase     ,attention_mask=lowercase     ,labels=lowercase)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.num_labels
             UpperCAmelCase_  :	List[Any]				      =		SqueezeBertForTokenClassification(config=lowercase)
             model.to(lowercase)
             model.eval()

             UpperCAmelCase_  :	Optional[int]				      =		model(lowercase     ,attention_mask=lowercase     ,labels=lowercase)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		self.num_choices
             UpperCAmelCase_  :	Union[str, Any]				      =		SqueezeBertForMultipleChoice(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Tuple				      =		input_ids.unsqueeze(1).expand(-1     ,self.num_choices     ,-1).contiguous()
             UpperCAmelCase_  :	Optional[Any]				      =		input_mask.unsqueeze(1).expand(-1     ,self.num_choices     ,-1).contiguous()
             UpperCAmelCase_  :	str				      =		model(
                 lowercase     ,attention_mask=lowercase     ,labels=lowercase     ,)
             self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.prepare_config_and_inputs()
             ((UpperCAmelCase_)      ,      (UpperCAmelCase_)      ,      (UpperCAmelCase_)      ,      (UpperCAmelCase_)      ,      (UpperCAmelCase_)      ,      (UpperCAmelCase_))  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Dict				      =		{"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict


@require_torch
class   snake_case_ (lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		SqueezeBertModelTester(self)
             UpperCAmelCase_  :	Optional[int]				      =		ConfigTester(self     ,config_class=lowercase     ,dim=37)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.run_common_tests()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_model(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





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


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




       """simple docstring"""






       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")

             UpperCAmelCase_  :	List[str]				      =		torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
             UpperCAmelCase_  :	Optional[Any]				      =		model(lowercase)[0]
             UpperCAmelCase_  :	Tuple				      =		torch.Size((1, 3))
             self.assertEqual(output.shape     ,lowercase)
             UpperCAmelCase_  :	Dict				      =		torch.tensor([[0.6401, -0.0349, -0.6041]])
             self.assertTrue(torch.allclose(lowercase     ,lowercase     ,atol=1E-4))





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase = 2000     ,lowercase = 0.15     ,lowercase = 0.01     ,lowercase = 1348.0     ,lowercase = 1E-5     ,lowercase = 1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		sigma_max

             # setable values
             UpperCAmelCase_  :	Optional[int]				      =		None

             self.set_sigmas(lowercase     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

             UpperCAmelCase_  :	List[Any]				      =		torch.linspace(1     ,lowercase     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		sigma_min if sigma_min is not None else self.config.sigma_min
             UpperCAmelCase_  :	int				      =		sigma_max if sigma_max is not None else self.config.sigma_max
             UpperCAmelCase_  :	Union[str, Any]				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowercase     ,lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             UpperCAmelCase_  :	Optional[int]				      =		torch.exp(torch.linspace(math.log(lowercase)     ,math.log(lowercase)     ,lowercase))
             UpperCAmelCase_  :	Any				      =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             return torch.where(
                 timesteps == 0     ,torch.zeros_like(t.to(timesteps.device))     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device)     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		timestep * torch.ones(
                 sample.shape[0]     ,device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
             UpperCAmelCase_  :	Tuple				      =		(timestep * (len(self.timesteps) - 1)).long()

             # mps requires indices to be in the same device, so we use cpu as is the default with cuda
             UpperCAmelCase_  :	Optional[int]				      =		timesteps.to(self.discrete_sigmas.device)

             UpperCAmelCase_  :	Optional[Any]				      =		self.discrete_sigmas[timesteps].to(sample.device)
             UpperCAmelCase_  :	Optional[Any]				      =		self.get_adjacent_sigma(lowercase     ,lowercase).to(sample.device)
             UpperCAmelCase_  :	Any				      =		torch.zeros_like(lowercase)
             UpperCAmelCase_  :	Dict				      =		(sigma**2 - adjacent_sigma**2) ** 0.5

             # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
             # also equation 47 shows the analog from SDE models to ancestral sampling methods
             UpperCAmelCase_  :	Dict				      =		diffusion.flatten()
             while len(diffusion.shape) < len(sample.shape):
                   UpperCAmelCase_  :	List[str]				      =		diffusion.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             UpperCAmelCase_  :	Union[str, Any]				      =		randn_tensor(
                 sample.shape     ,layout=sample.layout     ,generator=lowercase     ,device=sample.device     ,dtype=sample.dtype)
             UpperCAmelCase_  :	Any				      =		sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             UpperCAmelCase_  :	Tuple				      =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

             if not return_dict:
                   return (prev_sample, prev_sample_mean)

             return SdeVeOutput(prev_sample=lowercase     ,prev_sample_mean=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
             # sample noise for correction
             UpperCAmelCase_  :	int				      =		randn_tensor(sample.shape     ,layout=sample.layout     ,generator=lowercase).to(sample.device)

             # compute step size from the model_output, the noise, and the snr
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.norm(model_output.reshape(model_output.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	Optional[Any]				      =		torch.norm(noise.reshape(noise.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	List[Any]				      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
             UpperCAmelCase_  :	Optional[Any]				      =		step_size * torch.ones(sample.shape[0]).to(sample.device)
             # self.repeat_scalar(step_size, sample.shape[0])

             # compute corrected sample: model_output term and noise term
             UpperCAmelCase_  :	Any				      =		step_size.flatten()
             while len(step_size.shape) < len(sample.shape):
                   UpperCAmelCase_  :	Tuple				      =		step_size.unsqueeze(-1)
             UpperCAmelCase_  :	Dict				      =		sample + step_size * model_output
             UpperCAmelCase_  :	int				      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		timesteps.to(original_samples.device)
             UpperCAmelCase_  :	List[str]				      =		self.discrete_sigmas.to(original_samples.device)[timesteps]
             UpperCAmelCase_  :	Tuple				      =		(
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowercase) * sigmas[:, None, None, None]
             )
             UpperCAmelCase_  :	Tuple				      =		noise + original_samples
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps
