





"""simple docstring"""


import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       RemBertConfig,
       RemBertForCausalLM,
       RemBertForMaskedLM,
       RemBertForMultipleChoice,
       RemBertForQuestionAnswering,
       RemBertForSequenceClassification,
       RemBertForTokenClassification,
       RemBertModel,
   )
   from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=13      ,    __SCREAMING_SNAKE_CASE=7      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=99      ,    __SCREAMING_SNAKE_CASE=32      ,    __SCREAMING_SNAKE_CASE=18      ,    __SCREAMING_SNAKE_CASE=43      ,    __SCREAMING_SNAKE_CASE=5      ,    __SCREAMING_SNAKE_CASE=4      ,    __SCREAMING_SNAKE_CASE=37      ,    __SCREAMING_SNAKE_CASE="gelu"      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=512      ,    __SCREAMING_SNAKE_CASE=16      ,    __SCREAMING_SNAKE_CASE=2      ,    __SCREAMING_SNAKE_CASE=0.02      ,    __SCREAMING_SNAKE_CASE=3      ,    __SCREAMING_SNAKE_CASE=4      ,    __SCREAMING_SNAKE_CASE=None      ,    ):






        '''simple docstring'''

        snake_case__										= parent
        snake_case__										= batch_size
        snake_case__										= seq_length
        snake_case__										= is_training
        snake_case__										= use_input_mask
        snake_case__										= use_token_type_ids
        snake_case__										= use_labels
        snake_case__										= vocab_size
        snake_case__										= hidden_size
        snake_case__										= input_embedding_size
        snake_case__										= output_embedding_size
        snake_case__										= num_hidden_layers
        snake_case__										= num_attention_heads
        snake_case__										= intermediate_size
        snake_case__										= hidden_act
        snake_case__										= hidden_dropout_prob
        snake_case__										= attention_probs_dropout_prob
        snake_case__										= max_position_embeddings
        snake_case__										= type_vocab_size
        snake_case__										= type_sequence_label_size
        snake_case__										= initializer_range
        snake_case__										= num_labels
        snake_case__										= num_choices
        snake_case__										= scope

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

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

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

        snake_case__										= RemBertConfig(
            vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    input_embedding_size=self.input_embedding_size      ,    output_embedding_size=self.output_embedding_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=__SCREAMING_SNAKE_CASE      ,    initializer_range=self.initializer_range      ,    )

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        (
            (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      
        )										= self.prepare_config_and_inputs()

        snake_case__										= True
        snake_case__										= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
        snake_case__										= ids_tensor([self.batch_size, self.seq_length]      ,    vocab_size=2   )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= RemBertModel(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE   )
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE   )
        snake_case__										= model(__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= True
        snake_case__										= RemBertModel(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,    )
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    )
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    labels=__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= RemBertForMaskedLM(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    labels=__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= True
        snake_case__										= True
        snake_case__										= RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()

        # first forward pass
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,    use_cache=__SCREAMING_SNAKE_CASE      ,    )
        snake_case__										= outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        snake_case__										= ids_tensor((self.batch_size, 3)      ,    config.vocab_size   )
        snake_case__										= ids_tensor((self.batch_size, 3)      ,    vocab_size=2   )

        # append to next input_ids and
        snake_case__										= torch.cat([input_ids, next_tokens]      ,    dim=-1   )
        snake_case__										= torch.cat([input_mask, next_mask]      ,    dim=-1   )

        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    )["hidden_states"][0]
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,    past_key_values=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    )["hidden_states"][0]

        # select random slice
        snake_case__										= ids_tensor((1,)      ,    output_from_past.shape[-1]   ).item()
        snake_case__										= output_from_no_past[:, -3:, random_slice_idx].detach()
        snake_case__										= output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    atol=1e-3   )   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= RemBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    start_positions=__SCREAMING_SNAKE_CASE      ,    end_positions=__SCREAMING_SNAKE_CASE      ,    )
        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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.num_labels
        snake_case__										= RemBertForSequenceClassification(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    labels=__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.num_labels
        snake_case__										= RemBertForTokenClassification(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    labels=__SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels)   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.num_choices
        snake_case__										= RemBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        snake_case__										= input_ids.unsqueeze(1   ).expand(-1      ,    self.num_choices      ,    -1   ).contiguous()
        snake_case__										= token_type_ids.unsqueeze(1   ).expand(-1      ,    self.num_choices      ,    -1   ).contiguous()
        snake_case__										= input_mask.unsqueeze(1   ).expand(-1      ,    self.num_choices      ,    -1   ).contiguous()
        snake_case__										= model(
            __SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    labels=__SCREAMING_SNAKE_CASE      ,    )
        self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices)   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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



@require_torch
class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							Union[str, Any] 							=   (
           (
               RemBertModel,
               RemBertForMaskedLM,
               RemBertForCausalLM,
               RemBertForMultipleChoice,
               RemBertForQuestionAnswering,
               RemBertForSequenceClassification,
               RemBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       snake_case__ :							str 							=   (RemBertForCausalLM,) if is_torch_available() else ()
       snake_case__ :							Optional[int] 							=   (
           {
               """feature-extraction""": RemBertModel,
               """fill-mask""": RemBertForMaskedLM,
               """question-answering""": RemBertForQuestionAnswering,
               """text-classification""": RemBertForSequenceClassification,
               """text-generation""": RemBertForCausalLM,
               """token-classification""": RemBertForTokenClassification,
               """zero-shot""": RemBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= RemBertModelTester(self   )
        snake_case__										= ConfigTester(self      ,    config_class=__SCREAMING_SNAKE_CASE      ,    hidden_size=37   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
         snake_case__										= type
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        (
            (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      (
            snake_case__
        )							,      
        )										= self.model_tester.prepare_config_and_inputs_for_decoder()

        snake_case__										= None

        self.model_tester.create_and_check_model_as_decoder(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    )

       @slow
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__										= RemBertModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
         self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )



@require_torch
class    lowercase_ (   unittest.TestCase							):

       @slow
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= RemBertModel.from_pretrained("google/rembert"   )
        snake_case__										= torch.tensor([[312, 5_6498, 313, 2125, 313]]   )
        snake_case__										= torch.tensor([[0, 0, 0, 1, 1]]   )
        with torch.no_grad():
         snake_case__										= model(__SCREAMING_SNAKE_CASE      ,    token_type_ids=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE   )

        snake_case__										= 1152

        snake_case__										= torch.Size((1, 5, hidden_size)   )
        self.assertEqual(output["last_hidden_state"].shape      ,    __SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.tensor(
            [
                [
                    [0.0754, -0.2022, 0.1904],
                    [-0.3354, -0.3692, -0.4791],
                    [-0.2314, -0.6729, -0.0749],
                    [-0.0396, -0.3105, -0.4234],
                    [-0.1571, -0.0525, 0.5353],
                ]
            ]   )

        # Running on the original tf implementation gives slightly different results here.
        # Not clear why this variations is present
        # TODO: Find reason for discrepancy
        # expected_original_implementation = [[
        #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
        #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
        #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
        #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
        #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
        # ]]

        self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]      ,    __SCREAMING_SNAKE_CASE      ,    atol=1e-4   )   )








"""simple docstring"""


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

import numpy as np
import torch

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



@dataclass
class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							torch.FloatTensor
       snake_case__ :							torch.FloatTensor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=0.999		,   _UpperCAmelCase="cosine"		,   )			->     str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCAmelCase		):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_UpperCAmelCase		):
   return math.exp(t * -12.0		)

 else:
  raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

 snake_case__										= []
 for i in range(_UpperCAmelCase		):
  snake_case__										= i / num_diffusion_timesteps
  snake_case__										= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase		) / alpha_bar_fn(_UpperCAmelCase		)		,   _UpperCAmelCase		)		)
 return torch.tensor(_UpperCAmelCase		,   dtype=torch.floataa		)



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   1

       @register_to_config
       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1000      ,    __SCREAMING_SNAKE_CASE = 0.0001      ,    __SCREAMING_SNAKE_CASE = 0.02      ,    __SCREAMING_SNAKE_CASE = "linear"      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "linear":
         snake_case__										= torch.linspace(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__										= (
             torch.linspace(beta_start**0.5      ,    beta_end**0.5      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__										= betas_for_alpha_bar(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         snake_case__										= torch.linspace(-6      ,    6      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= torch.sigmoid(__SCREAMING_SNAKE_CASE   ) * (beta_end - beta_start) + beta_start
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

        snake_case__										= 1.0 - self.betas
        snake_case__										= torch.cumprod(self.alphas      ,    dim=0   )
        snake_case__										= torch.tensor(1.0   )

        snake_case__										= torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case__										= 1.0

        # setable values
        snake_case__										= None
        snake_case__										= torch.from_numpy(np.arange(0      ,    __SCREAMING_SNAKE_CASE   )[::-1].copy()   )

        snake_case__										= eta

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= min(self.config.num_train_timesteps      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_inference_steps

        snake_case__										= []

        snake_case__										= {}
        for j in range(0      ,    num_inference_steps - jump_length      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= jump_n_sample - 1

        snake_case__										= num_inference_steps
        while t >= 1:
         snake_case__										= t - 1
         timesteps.append(__SCREAMING_SNAKE_CASE   )

         if jumps.get(__SCREAMING_SNAKE_CASE      ,    0   ) > 0:
          snake_case__										= jumps[t] - 1
          for _ in range(__SCREAMING_SNAKE_CASE   ):
           snake_case__										= t + 1
           timesteps.append(__SCREAMING_SNAKE_CASE   )

        snake_case__										= np.array(__SCREAMING_SNAKE_CASE   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t
        snake_case__										= 1 - alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
        # variance to pred_sample
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case__										= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= timestep
        snake_case__										= timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case__										= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         snake_case__										= torch.clamp(__SCREAMING_SNAKE_CASE      ,    -1      ,    1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case__										= model_output.device
        snake_case__										= randn_tensor(model_output.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=model_output.dtype   )
        snake_case__										= self.eta * self._get_variance(__SCREAMING_SNAKE_CASE   ) ** 0.5

        snake_case__										= 0
        if t > 0 and self.eta > 0:
         snake_case__										= std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

        return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,    pred_original_sample=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__SCREAMING_SNAKE_CASE   ):
         snake_case__										= self.betas[timestep + i]
         if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          snake_case__										= randn_tensor(sample.shape      ,    dtype=sample.dtype      ,    generator=__SCREAMING_SNAKE_CASE   )
          snake_case__										= noise.to(sample.device   )
         else:
          snake_case__										= randn_tensor(sample.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=sample.device      ,    dtype=sample.dtype   )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         snake_case__										= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."   )

       def __len__(	self   ):






        '''simple docstring'''

        return self.config.num_train_timesteps

