


import unittest

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

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


if is_torch_available():
  import torch

  from transformers import BertGenerationDecoder, BertGenerationEncoder







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_0		,     UpperCAmelCase=0.02		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     )     ->   str:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =use_labels
      _lowercase							      =scope





     def      __A					(self       )     ->   int:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

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

      if self.use_labels:
       _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =self.get_config()

      return config, input_ids, input_mask, token_labels





     def      __A					(self       )     ->   Any:
      return BertGenerationConfig(
          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		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     is_decoder=UpperCAmelCase		,     initializer_range=self.initializer_range		,     )





     def      __A					(self       )     ->   Dict:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.prepare_config_and_inputs()

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

      return (
          config,
          input_ids,
          input_mask,
          token_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   Tuple:
      _lowercase							      =BertGenerationEncoder(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   List[str]:
      _lowercase							      =True
      _lowercase							      =BertGenerationEncoder(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     )
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   Dict:
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =BertGenerationDecoder(config=UpperCAmelCase       ).to(UpperCAmelCase       ).eval()

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

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

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

      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     past_key_values=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     *UpperCAmelCase		,     )     ->   Optional[int]:
      _lowercase							      =BertGenerationDecoder(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )





     def      __A					(self       )     ->   Tuple:
      _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =self.prepare_config_and_inputs()
      _lowercase							      ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(BertGenerationDecoder,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
         if is_torch_available()
         else {}
     )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =BertGenerationEncoderTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   List[str]:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs()
      _lowercase							      ='''bert'''
      self.model_tester.create_and_check_model(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase       )





     def      __A					(self       )     ->   int:
      # This regression test was failing with PyTorch < 1.3
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs_for_decoder()

      _lowercase							      =None

      self.model_tester.create_and_check_model_as_decoder(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      self.assertIsNotNone(UpperCAmelCase       )







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   List[str]:
      _lowercase							      =BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _lowercase							      =torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]       )
      with torch.no_grad():
       _lowercase							      =model(UpperCAmelCase       )[0]
      _lowercase							      =torch.Size([1, 8, 1_0_2_4]       )
      self.assertEqual(output.shape		,     UpperCAmelCase       )
      _lowercase							      =torch.tensor(
          [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )       )







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   Dict:
      _lowercase							      =BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _lowercase							      =torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]       )
      with torch.no_grad():
       _lowercase							      =model(UpperCAmelCase       )[0]
      _lowercase							      =torch.Size([1, 8, 5_0_3_5_8]       )
      self.assertEqual(output.shape		,     UpperCAmelCase       )
      _lowercase							      =torch.tensor(
          [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )       )






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




