"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			Tuple           =		AutoencoderKL
 _snake_case   :			int           =		"""sample"""
 _snake_case   :			List[str]           =		1E-2


 @property
 def 		a__ (				self		):
        __a     				=  4
        __a     				=  3
        __a     				=  (32, 32)

        __a     				=  floats_tensor((batch_size, num_channels) + sizes		).to(lowerCamelCase		)

        return {"sample": image}


 @property
 def 		a__ (				self		):
        return (3, 32, 32)


 @property
 def 		a__ (				self		):
        return (3, 32, 32)


 def 		a__ (				self		):
        __a     				=  {
            "block_out_channels": [32, 64],
            "in_channels": 3,
            "out_channels": 3,
            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
            "latent_channels": 4,
        }
        __a     				=  self.dummy_input
        return init_dict, inputs_dict


 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        pass


 @unittest.skipIf(torch_device == "mps"  ,						"Gradient checkpointing skipped on MPS"		)
 def 		a__ (				self		):
        # enable deterministic behavior for gradient checkpointing
        __a				, __a     				=  self.prepare_init_args_and_inputs_for_common()
        __a     				=  self.model_class(**lowerCamelCase		)
        model.to(lowerCamelCase		)

        assert not model.is_gradient_checkpointing and model.training

        __a     				=  model(**lowerCamelCase		).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        __a     				=  torch.randn_like(lowerCamelCase		)
        __a     				=  (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        __a     				=  self.model_class(**lowerCamelCase		)
        # clone model
        model_a.load_state_dict(model.state_dict()		)
        model_a.to(lowerCamelCase		)
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        __a     				=  model_a(**lowerCamelCase		).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        __a     				=  (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1E-5		)
        __a     				=  dict(model.named_parameters()		)
        __a     				=  dict(model_a.named_parameters()		)
        for name, param in named_params.items():
               self.assertTrue(torch_all_close(param.grad.data  ,						named_params_a[name].grad.data  ,						atol=5E-5		)		)


 def 		a__ (				self		):
        __a				, __a     				=  AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"  ,						output_loading_info=lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)
        self.assertEqual(len(loading_info["missing_keys"]		)  ,						0		)

        model.to(lowerCamelCase		)
        __a     				=  model(**self.dummy_input		)

        assert image is not None, "Make sure output is not None"






 def 		a__ (				self		):
        __a     				=  AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
        __a     				=  model.to(lowerCamelCase		)
        model.eval()

        if torch_device == "mps":
               __a     				=  torch.manual_seed(0		)
        else:
               __a     				=  torch.Generator(device=lowerCamelCase		).manual_seed(0		)

        __a     				=  torch.randn(
            1  ,						model.config.in_channels  ,						model.config.sample_size  ,						model.config.sample_size  ,						generator=torch.manual_seed(0		)  ,						)
        __a     				=  image.to(lowerCamelCase		)
        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						sample_posterior=lowerCamelCase  ,						generator=lowerCamelCase		).sample

        __a     				=  output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
               __a     				=  torch.tensor(
                   [
                       -4.0078E-01,
                       -3.8323E-04,
                       -1.2681E-01,
                       -1.1462E-01,
                       2.0095E-01,
                       1.0893E-01,
                       -8.8247E-02,
                       -3.0361E-01,
                       -9.8644E-03,
                   ]		)
        elif torch_device == "cpu":
               __a     				=  torch.tensor(
                   [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
        else:
               __a     				=  torch.tensor(
                   [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]		)

        self.assertTrue(torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						rtol=1E-2		)		)






@slow
class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase		) for s in shape]		)}.npy"


 def 		a__ (				self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 		a__ (				self  ,						lowerCamelCase=0  ,						lowerCamelCase=(4, 3, 512, 512)  ,						lowerCamelCase=False		):
        __a     				=  torch.floataa if fpaa else torch.floataa
        __a     				=  torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase  ,						lowerCamelCase		)		)		).to(lowerCamelCase		).to(lowerCamelCase		)
        return image


 def 		a__ (				self  ,						lowerCamelCase="CompVis/stable-diffusion-v1-4"  ,						lowerCamelCase=False		):
        __a     				=  "fp16" if fpaa else None
        __a     				=  torch.floataa if fpaa else torch.floataa

        __a     				=  AutoencoderKL.from_pretrained(
            lowerCamelCase  ,						subfolder="vae"  ,						torch_dtype=lowerCamelCase  ,						revision=lowerCamelCase  ,						)
        model.to(lowerCamelCase		).eval()

        return model


 def 		a__ (				self  ,						lowerCamelCase=0		):
        if torch_device == "mps":
               return torch.manual_seed(lowerCamelCase		)
        return torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						generator=lowerCamelCase  ,						sample_posterior=lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
        __a     				=  torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=3E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
         [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						fpaa=lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase  ,						generator=lowerCamelCase  ,						sample_posterior=lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-2		)


 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model(lowerCamelCase		).sample

        assert sample.shape == image.shape

        __a     				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
        __a     				=  torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=3E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
         [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        __a     				=  sample[-1, -2:, :2, -2:].flatten().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-3		)


 @parameterized.expand(
     [
         # fmt: off
         [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
         [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)  ,						fpaa=lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        __a     				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=5E-3		)


 @parameterized.expand([(13,), (16,), (27,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()  ,						reason="xformers is not required when using PyTorch 2.0."		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model(fpaa=lowerCamelCase		)
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)  ,						fpaa=lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-1		)


 @parameterized.expand([(13,), (16,), (37,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()  ,						reason="xformers is not required when using PyTorch 2.0."		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase  ,						shape=(3, 4, 64, 64)		)

        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a     				=  model.decode(lowerCamelCase		).sample

        assert list(sample.shape		) == [3, 3, 512, 512]

        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-2		)






 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
         [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
         # fmt: on
     ]		)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.get_sd_vae_model()
        __a     				=  self.get_sd_image(lowerCamelCase		)
        __a     				=  self.get_generator(lowerCamelCase		)

        with torch.no_grad():
               __a     				=  model.encode(lowerCamelCase		).latent_dist
               __a     				=  dist.sample(generator=lowerCamelCase		)

        assert list(sample.shape		) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        __a     				=  sample[0, -1, -3:, -3:].flatten().cpu()
        __a     				=  torch.tensor(lowerCamelCase		)

        __a     				=  3E-3 if torch_device != "mps" else 1E-2
        assert torch_all_close(lowerCamelCase  ,						lowerCamelCase  ,						atol=lowerCamelCase		)





"""simple docstring"""



import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 					_lowerCamelCase(					a							):
       __a     				=  torch.exp(a							)
       __a     				=  torch.sum(a ,  dim=1							)  # sum of exp(x_i)
       __a     				=  torch.sum(x * exp_x ,  dim=1							)  # sum of x_i * exp(x_i)
       return torch.log(a							) - B / A






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  config.output_attentions
        __a     				=  config.output_hidden_states
        __a     				=  nn.ModuleList([BertLayer(lowerCamelCase		) for _ in range(config.num_hidden_layers		)]		)
        __a     				=  nn.ModuleList([BertHighway(lowerCamelCase		) for _ in range(config.num_hidden_layers		)]		)

        __a     				=  [-1 for _ in range(config.num_hidden_layers		)]


 def 		a__ (				self  ,						lowerCamelCase		):
        if (type(lowerCamelCase		) is float) or (type(lowerCamelCase		) is int):
               for i in range(len(self.early_exit_entropy		)		):
                      __a     				=  x
        else:
               __a     				=  x


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  pooler.state_dict()
        for highway in self.highway:
               for name, param in highway.pooler.state_dict().items():
                      param.copy_(loaded_model[name]		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						):
        __a     				=  ()
        __a     				=  ()
        __a     				=  ()
        for i, layer_module in enumerate(self.layer		):
               if self.output_hidden_states:
                      __a     				=  all_hidden_states + (hidden_states,)

               __a     				=  layer_module(
                   lowerCamelCase  ,						lowerCamelCase  ,						head_mask[i]  ,						lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  layer_outputs[0]

               if self.output_attentions:
                      __a     				=  all_attentions + (layer_outputs[1],)

               __a     				=  (hidden_states,)
               if self.output_hidden_states:
                      __a     				=  current_outputs + (all_hidden_states,)
               if self.output_attentions:
                      __a     				=  current_outputs + (all_attentions,)

               __a     				=  self.highway[i](lowerCamelCase		)
               # logits, pooled_output

               if not self.training:
                      __a     				=  highway_exit[0]
                      __a     				=  entropy(lowerCamelCase		)
                      __a     				=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                      __a     				=  all_highway_exits + (highway_exit,)

                      if highway_entropy < self.early_exit_entropy[i]:
                             __a     				=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                             raise HighwayException(lowerCamelCase  ,						i + 1		)
               else:
                      __a     				=  all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
               __a     				=  all_hidden_states + (hidden_states,)

        __a     				=  (hidden_states,)
        if self.output_hidden_states:
               __a     				=  outputs + (all_hidden_states,)
        if self.output_attentions:
               __a     				=  outputs + (all_attentions,)

        __a     				=  outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """,		snake_case_,		)
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config

        __a     				=  BertEmbeddings(lowerCamelCase		)
        __a     				=  DeeBertEncoder(lowerCamelCase		)
        __a     				=  BertPooler(lowerCamelCase		)

        self.init_weights()


 def 		a__ (				self		):
        self.encoder.init_highway_pooler(self.pooler		)


 def 		a__ (				self		):
        return self.embeddings.word_embeddings


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  value


 def 		a__ (				self  ,						lowerCamelCase		):
        for layer, heads in heads_to_prune.items():
               self.encoder.layer[layer].attention.prune_heads(lowerCamelCase		)






 @add_start_docstrings_to_model_forward(lowerCamelCase		)
 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						):
        if input_ids is not None and inputs_embeds is not None:
               raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
        elif input_ids is not None:
               __a     				=  input_ids.size()
        elif inputs_embeds is not None:
               __a     				=  inputs_embeds.size()[:-1]
        else:
               raise ValueError("You have to specify either input_ids or inputs_embeds"		)

        __a     				=  input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
               __a     				=  torch.ones(lowerCamelCase  ,						device=lowerCamelCase		)
        if encoder_attention_mask is None:
               __a     				=  torch.ones(lowerCamelCase  ,						device=lowerCamelCase		)
        if token_type_ids is None:
               __a     				=  torch.zeros(lowerCamelCase  ,						dtype=torch.long  ,						device=lowerCamelCase		)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        __a     				=  self.get_extended_attention_mask(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
               __a     				=  encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
               __a     				=  encoder_attention_mask[:, None, None, :]

        __a     				=  encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        __a     				=  (1.0 - encoder_extended_attention_mask) * -1_0000.0

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        __a     				=  self.get_head_mask(lowerCamelCase  ,						self.config.num_hidden_layers		)

        __a     				=  self.embeddings(
            input_ids=lowerCamelCase  ,						position_ids=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						inputs_embeds=lowerCamelCase		)
        __a     				=  self.encoder(
            lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						head_mask=lowerCamelCase  ,						encoder_hidden_states=lowerCamelCase  ,						encoder_attention_mask=lowerCamelCase  ,						)
        __a     				=  encoder_outputs[0]
        __a     				=  self.pooler(lowerCamelCase		)

        __a     				=  (
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  message
        __a     				=  exit_layer  # start from 1!






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  BertPooler(lowerCamelCase		)
        __a     				=  nn.Dropout(config.hidden_dropout_prob		)
        __a     				=  nn.Linear(config.hidden_size  ,						config.num_labels		)






 def 		a__ (				self  ,						lowerCamelCase		):
        # Pooler
        __a     				=  encoder_outputs[0]
        __a     				=  self.pooler(lowerCamelCase		)
        # "return" pooler_output

        # BertModel
        __a     				=  (pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        __a     				=  bmodel_output[1]

        __a     				=  self.dropout(lowerCamelCase		)
        __a     				=  self.classifier(lowerCamelCase		)

        return logits, pooled_output






@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """,		snake_case_,		)
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config.num_labels
        __a     				=  config.num_hidden_layers

        __a     				=  DeeBertModel(lowerCamelCase		)
        __a     				=  nn.Dropout(config.hidden_dropout_prob		)
        __a     				=  nn.Linear(config.hidden_size  ,						self.config.num_labels		)

        self.init_weights()






 @add_start_docstrings_to_model_forward(lowerCamelCase		)
 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=-1  ,						lowerCamelCase=False  ,						):
        __a     				=  self.num_layers
        try:
               __a     				=  self.bert(
                   lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						position_ids=lowerCamelCase  ,						head_mask=lowerCamelCase  ,						inputs_embeds=lowerCamelCase  ,						)
               # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

               __a     				=  outputs[1]

               __a     				=  self.dropout(lowerCamelCase		)
               __a     				=  self.classifier(lowerCamelCase		)
               __a     				=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
               __a     				=  e.message
               __a     				=  e.exit_layer
               __a     				=  outputs[0]

        if not self.training:
               __a     				=  entropy(lowerCamelCase		)
               __a     				=  []
               __a     				=  []
        if labels is not None:
               if self.num_labels == 1:
                      #  We are doing regression
                      __a     				=  MSELoss()
                      __a     				=  loss_fct(logits.view(-1		)  ,						labels.view(-1		)		)
               else:
                      __a     				=  CrossEntropyLoss()
                      __a     				=  loss_fct(logits.view(-1  ,						self.num_labels		)  ,						labels.view(-1		)		)

               # work with highway exits
               __a     				=  []
               for highway_exit in outputs[-1]:
                      __a     				=  highway_exit[0]
                      if not self.training:
                             highway_logits_all.append(lowerCamelCase		)
                             highway_entropy.append(highway_exit[2]		)
                      if self.num_labels == 1:
                             #  We are doing regression
                             __a     				=  MSELoss()
                             __a     				=  loss_fct(highway_logits.view(-1		)  ,						labels.view(-1		)		)
                      else:
                             __a     				=  CrossEntropyLoss()
                             __a     				=  loss_fct(highway_logits.view(-1  ,						self.num_labels		)  ,						labels.view(-1		)		)
                      highway_losses.append(lowerCamelCase		)

               if train_highway:
                      __a     				=  (sum(highway_losses[:-1]		),) + outputs
                      # exclude the final highway, of course
               else:
                      __a     				=  (loss,) + outputs
        if not self.training:
               __a     				=  outputs + ((original_entropy, highway_entropy), exit_layer)
               if output_layer >= 0:
                      __a     				=  (
                          (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                      )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)
