




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__lowerCAmelCase		:				List[Any]       			=		'platform'

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)




def 			__magic_name__				( A		:					Optional[Any],  A		:					Tuple,  A		:					Optional[int]=None,  A		:					Dict=None,  A		:					Tuple=None,  A		:					Any=None,  A		:					str=None,  A		:					Optional[Any]=None,  ):






			'''simple docstring'''







			if attention_mask is None:
						a    =			np.where(input_ids != config.pad_token_id,  1,  0   )
			if decoder_attention_mask is None:
						a    =			np.where(decoder_input_ids != config.pad_token_id,  1,  0   )
			if head_mask is None:
						a    =			np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						a    =			np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						a    =			np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[Any]=13					,      __lowerCamelCase				:     Union[str, Any]=7					,      __lowerCamelCase				:     Optional[int]=True					,      __lowerCamelCase				:     str=False					,      __lowerCamelCase				:     Tuple=99					,      __lowerCamelCase				:     Any=16					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     int=4					,      __lowerCamelCase				:     Union[str, Any]=4					,      __lowerCamelCase				:     List[Any]="gelu"					,      __lowerCamelCase				:     Tuple=0.1					,      __lowerCamelCase				:     Optional[Any]=0.1					,      __lowerCamelCase				:     Optional[int]=32					,      __lowerCamelCase				:     Optional[int]=2					,      __lowerCamelCase				:     Dict=1					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     Any=0.02					,      )					->     Optional[int]:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			eos_token_id
							a    =			pad_token_id
							a    =			bos_token_id
							a    =			initializer_range





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							a    =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size							)					,      3					,      self.vocab_size							)
							a    =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,      dtype=np.intaa							))					,      -1							)

							a    =			shift_tokens_right(__lowerCamelCase					,      1					,      2							)

							a    =			BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,      d_model=self.hidden_size					,      encoder_layers=self.num_hidden_layers					,      decoder_layers=self.num_hidden_layers					,      encoder_attention_heads=self.num_attention_heads					,      decoder_attention_heads=self.num_attention_heads					,      encoder_ffn_dim=self.intermediate_size					,      decoder_ffn_dim=self.intermediate_size					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      eos_token_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      initializer_range=self.initializer_range					,      use_cache=__lowerCamelCase					,      )
							a    =			prepare_blenderbot_inputs_dict(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							return config, inputs_dict





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Dict:
							a    ,		a    =			self.prepare_config_and_inputs()
							return config, inputs_dict





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]							)					->     Optional[Any]:
							a    =			20
							a    =			model_class_name(__lowerCamelCase							)

							a    =			model.encode(inputs_dict["input_ids"]							)

							a    ,		a    =			(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							a    =			model.init_cache(decoder_input_ids.shape[0]					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,      dtype="i4"							)

							a    =			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )
							a    =			model.decode(
							    decoder_input_ids[:, :-1]					,      __lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase					,      decoder_position_ids=__lowerCamelCase					,      )

							a    =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="i4"							)
							a    =			model.decode(
							    decoder_input_ids[:, -1:]					,      __lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase					,      past_key_values=outputs_cache.past_key_values					,      decoder_position_ids=__lowerCamelCase					,      )

							a    =			model.decode(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
							self.parent.assertTrue(diff < 1e-3					,      msg=f"""Max diff is {diff}"""							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     int							)					->     Any:
							a    =			20
							a    =			model_class_name(__lowerCamelCase							)

							a    =			model.encode(inputs_dict["input_ids"]							)

							a    ,		a    =			(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							a    =			jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
							    ]					,      axis=-1					,      )

							a    =			model.init_cache(decoder_input_ids.shape[0]					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )

							a    =			model.decode(
							    decoder_input_ids[:, :-1]					,      __lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase					,      decoder_position_ids=__lowerCamelCase					,      )
							a    =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="i4"							)
							a    =			model.decode(
							    decoder_input_ids[:, -1:]					,      __lowerCamelCase					,      past_key_values=outputs_cache.past_key_values					,      decoder_attention_mask=__lowerCamelCase					,      decoder_position_ids=__lowerCamelCase					,      )

							a    =			model.decode(__lowerCamelCase					,      __lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase							)

							a    =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
							self.parent.assertTrue(diff < 1e-3					,      msg=f"""Max diff is {diff}"""							)




@require_flax
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					99





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    =			np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]					,      dtype=np.intaa					,      )

							a    =			input_ids.shape[0]
							a    =			BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,      d_model=24					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=32					,      decoder_ffn_dim=32					,      max_position_embeddings=48					,      eos_token_id=2					,      pad_token_id=1					,      bos_token_id=0					,      )
							return config, input_ids, batch_size





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							a    ,		a    ,		a    =			self._get_config_and_data()
							a    =			FlaxBlenderbotSmallForConditionalGeneration(__lowerCamelCase							)
							a    =			lm_model(input_ids=__lowerCamelCase							)
							a    =			(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Tuple:
							a    =			BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,      d_model=14					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=8					,      decoder_ffn_dim=8					,      max_position_embeddings=48					,      )
							a    =			FlaxBlenderbotSmallForConditionalGeneration(__lowerCamelCase							)
							a    =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,      dtype=np.intaa							)
							a    =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,      dtype=np.intaa							)
							a    =			lm_model(input_ids=__lowerCamelCase					,      decoder_input_ids=__lowerCamelCase							)
							a    =			(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							a    =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,      dtype=np.intaa							)
							a    =			shift_tokens_right(__lowerCamelCase					,      1					,      2							)
							a    =			np.equal(__lowerCamelCase					,      1							).astype(np.floataa							).sum()
							a    =			np.equal(__lowerCamelCase					,      1							).astype(np.floataa							).sum()
							self.assertEqual(shifted.shape					,      input_ids.shape							)
							self.assertEqual(__lowerCamelCase					,      n_pad_before - 1							)
							self.assertTrue(np.equal(shifted[:, 0]					,      2							).all()							)




@require_flax
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase    ,       _UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					(
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :							int							 =					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[Any]:
							a    =			FlaxBlenderbotSmallModelTester(self							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__							):
													a    =			self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase							)
													a    =			model_class(__lowerCamelCase							)

													@jax.jit
													def encode_jitted(__lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Optional[int]							):
																return model.encode(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase							)

													with self.subTest("JIT Enabled"							):
																a    =			encode_jitted(**__lowerCamelCase							).to_tuple()

													with self.subTest("JIT Disabled"							):
																with jax.disable_jit():
																			a    =			encode_jitted(**__lowerCamelCase							).to_tuple()

													self.assertEqual(len(__lowerCamelCase							)					,      len(__lowerCamelCase							)							)
													for jitted_output, output in zip(__lowerCamelCase					,      __lowerCamelCase							):
																self.assertEqual(jitted_output.shape					,      output.shape							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__							):
													a    =			model_class(__lowerCamelCase							)
													a    =			model.encode(inputs_dict["input_ids"]					,      inputs_dict["attention_mask"]							)

													a    =			{
													    "decoder_input_ids": inputs_dict["decoder_input_ids"],
													    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
													    "encoder_outputs": encoder_outputs,
													}

													@jax.jit
													def decode_jitted(__lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Dict							):
																return model.decode(
																    decoder_input_ids=__lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase					,      encoder_outputs=__lowerCamelCase					,      )

													with self.subTest("JIT Enabled"							):
																a    =			decode_jitted(**__lowerCamelCase							).to_tuple()

													with self.subTest("JIT Disabled"							):
																with jax.disable_jit():
																			a    =			decode_jitted(**__lowerCamelCase							).to_tuple()

													self.assertEqual(len(__lowerCamelCase							)					,      len(__lowerCamelCase							)							)
													for jitted_output, output in zip(__lowerCamelCase					,      __lowerCamelCase							):
																self.assertEqual(jitted_output.shape					,      output.shape							)





				@slow
				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     str:
							for model_class_name in self.all_model_classes:
										a    =			model_class_name.from_pretrained("facebook/blenderbot_small-90M"							)
										# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
										a    =			np.ones((1, 1)							) * model.config.eos_token_id
										a    =			model(__lowerCamelCase							)
										self.assertIsNotNone(__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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       List[Any]  			=     torch.exp(_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     torch.sum(_lowerCamelCase			,dim=1	)  # sum of exp(x_i)
  _lowerCAmelCase						:       Dict  			=     torch.sum(x * exp_x			,dim=1	)  # sum of x_i * exp(x_i)
  return torch.log(_lowerCamelCase	) - B / A
class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       int  			=     config.output_attentions
       _lowerCAmelCase						:       Any  			=     config.output_hidden_states
       _lowerCAmelCase						:       List[Any]  			=     nn.ModuleList([BertLayer(a__  ) for _ in range(config.num_hidden_layers  )]  )
       _lowerCAmelCase						:       Any  			=     nn.ModuleList([BertHighway(a__  ) for _ in range(config.num_hidden_layers  )]  )

       _lowerCAmelCase						:       str  			=     [-1 for _ in range(config.num_hidden_layers  )]






     def     __A  (		self				,     a__  ):
       if (type(a__  ) is float) or (type(a__  ) is int):
         for i in range(len(self.early_exit_entropy  )  ):
           _lowerCAmelCase						:       Tuple  			=     x
       else:
         _lowerCAmelCase						:       Optional[int]  			=     x






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     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				,     a__				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     ):
       _lowerCAmelCase						:       Any  			=     ()
       _lowerCAmelCase						:       Optional[int]  			=     ()
       _lowerCAmelCase						:       List[Any]  			=     ()
       for i, layer_module in enumerate(self.layer  ):
         if self.output_hidden_states:
           _lowerCAmelCase						:       str  			=     all_hidden_states + (hidden_states,)

         _lowerCAmelCase						:       List[str]  			=     layer_module(
             a__				,     a__				,     head_mask[i]				,     a__				,     a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     layer_outputs[0]

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

         _lowerCAmelCase						:       Optional[int]  			=     (hidden_states,)
         if self.output_hidden_states:
           _lowerCAmelCase						:       Union[str, Any]  			=     current_outputs + (all_hidden_states,)
         if self.output_attentions:
           _lowerCAmelCase						:       Optional[int]  			=     current_outputs + (all_attentions,)

         _lowerCAmelCase						:       Optional[Any]  			=     self.highway[i](a__  )
         # logits, pooled_output

         if not self.training:
           _lowerCAmelCase						:       Tuple  			=     highway_exit[0]
           _lowerCAmelCase						:       Any  			=     entropy(a__  )
           _lowerCAmelCase						:       Optional[Any]  			=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           _lowerCAmelCase						:       Union[str, Any]  			=     all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
             _lowerCAmelCase						:       List[str]  			=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
             raise HighwayException(a__				,     i + 1  )
         else:
           _lowerCAmelCase						:       Dict  			=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[Any]  			=     all_hidden_states + (hidden_states,)

       _lowerCAmelCase						:       List[Any]  			=     (hidden_states,)
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     outputs + (all_hidden_states,)
       if self.output_attentions:
         _lowerCAmelCase						:       Any  			=     outputs + (all_attentions,)

       _lowerCAmelCase						:       Optional[int]  			=     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). "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config

       _lowerCAmelCase						:       Tuple  			=     BertEmbeddings(a__  )
       _lowerCAmelCase						:       Tuple  			=     DeeBertEncoder(a__  )
       _lowerCAmelCase						:       List[str]  			=     BertPooler(a__  )

       self.init_weights()






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






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     value






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






     @add_start_docstrings_to_model_forward(a__  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=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:
         _lowerCAmelCase						:       Any  			=     input_ids.size()
       elif inputs_embeds is not None:
         _lowerCAmelCase						:       List[str]  			=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

       _lowerCAmelCase						:       str  			=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _lowerCAmelCase						:       List[Any]  			=     torch.ones(a__				,     device=a__  )
       if encoder_attention_mask is None:
         _lowerCAmelCase						:       Optional[Any]  			=     torch.ones(a__				,     device=a__  )
       if token_type_ids is None:
         _lowerCAmelCase						:       Dict  			=     torch.zeros(a__				,     dtype=torch.long				,     device=a__  )

       # 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.
       _lowerCAmelCase						:       torch.Tensor  			=     self.get_extended_attention_mask(a__				,     a__				,     a__  )

       # 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:
         _lowerCAmelCase						:       Dict  			=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
         _lowerCAmelCase						:       Tuple  			=     encoder_attention_mask[:, None, None, :]

       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       _lowerCAmelCase						:       Optional[Any]  			=     (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

       # 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]
       _lowerCAmelCase						:       Optional[int]  			=     self.get_head_mask(a__				,     self.config.num_hidden_layers  )

       _lowerCAmelCase						:       Dict  			=     self.embeddings(
           input_ids=a__				,     position_ids=a__				,     token_type_ids=a__				,     inputs_embeds=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.encoder(
           a__				,     attention_mask=a__				,     head_mask=a__				,     encoder_hidden_states=a__				,     encoder_attention_mask=a__				,     )
       _lowerCAmelCase						:       Dict  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Union[str, Any]  			=     self.pooler(a__  )

       _lowerCAmelCase						:       Dict  			=     (
           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 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     message
       _lowerCAmelCase						:       str  			=     exit_layer  # start from 1!







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Any  			=     BertPooler(a__  )
       _lowerCAmelCase						:       str  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Linear(config.hidden_size				,     config.num_labels  )






     def     __A  (		self				,     a__  ):
       # Pooler
       _lowerCAmelCase						:       Tuple  			=     encoder_outputs[0]
       _lowerCAmelCase						:       int  			=     self.pooler(a__  )
       # "return" pooler_output

       # BertModel
       _lowerCAmelCase						:       Union[str, Any]  			=     (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       _lowerCAmelCase						:       Optional[int]  			=     bmodel_output[1]

       _lowerCAmelCase						:       Tuple  			=     self.dropout(a__  )
       _lowerCAmelCase						:       Dict  			=     self.classifier(a__  )

       return logits, pooled_output







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






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[str]  			=     config.num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     config.num_hidden_layers

       _lowerCAmelCase						:       str  			=     DeeBertModel(a__  )
       _lowerCAmelCase						:       Tuple  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       List[Any]  			=     nn.Linear(config.hidden_size				,     self.config.num_labels  )

       self.init_weights()






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

         _lowerCAmelCase						:       Any  			=     outputs[1]

         _lowerCAmelCase						:       Optional[int]  			=     self.dropout(a__  )
         _lowerCAmelCase						:       List[str]  			=     self.classifier(a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
         _lowerCAmelCase						:       Tuple  			=     e.message
         _lowerCAmelCase						:       int  			=     e.exit_layer
         _lowerCAmelCase						:       Union[str, Any]  			=     outputs[0]

       if not self.training:
         _lowerCAmelCase						:       Tuple  			=     entropy(a__  )
         _lowerCAmelCase						:       Optional[int]  			=     []
         _lowerCAmelCase						:       Optional[Any]  			=     []
       if labels is not None:
         if self.num_labels == 1:
           #  We are doing regression
           _lowerCAmelCase						:       Tuple  			=     MSELoss()
           _lowerCAmelCase						:       int  			=     loss_fct(logits.view(-1  )				,     labels.view(-1  )  )
         else:
           _lowerCAmelCase						:       Any  			=     CrossEntropyLoss()
           _lowerCAmelCase						:       Optional[int]  			=     loss_fct(logits.view(-1				,     self.num_labels  )				,     labels.view(-1  )  )

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

         if train_highway:
           _lowerCAmelCase						:       List[Any]  			=     (sum(highway_losses[:-1]  ),) + outputs
           # exclude the final highway, of course
         else:
           _lowerCAmelCase						:       Any  			=     (loss,) + outputs
       if not self.training:
         _lowerCAmelCase						:       Dict  			=     outputs + ((original_entropy, highway_entropy), exit_layer)
         if output_layer >= 0:
           _lowerCAmelCase						:       Dict  			=     (
               (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)




