

'''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       lowercase					(     __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]   					=   torch.exp(__a							)
					UpperCAmelCase		:							List[Any]   					=   torch.sum(__a ,    dim=1							)  # sum of exp(x_i)
					UpperCAmelCase		:							Union[str, Any]   					=   torch.sum(x * exp_x ,    dim=1							)  # sum of x_i * exp(x_i)
					return torch.log(__a							) - B / A







class   UpperCamelCase__    (   nn.Module   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case			):


							'''simple docstring'''

							super().__init__()
							UpperCAmelCase		:							Dict   					=   config.output_attentions
							UpperCAmelCase		:							Dict   					=   config.output_hidden_states
							UpperCAmelCase		:							List[Any]   					=   nn.ModuleList([BertLayer(UpperCAmelCase_			) for _ in range(config.num_hidden_layers			)]			)
							UpperCAmelCase		:							str   					=   nn.ModuleList([BertHighway(UpperCAmelCase_			) for _ in range(config.num_hidden_layers			)]			)

							UpperCAmelCase		:							int   					=   [-1 for _ in range(config.num_hidden_layers			)]







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							if (type(UpperCAmelCase_			) is float) or (type(UpperCAmelCase_			) is int):
												for i in range(len(self.early_exit_entropy			)			):
																	UpperCAmelCase		:							Optional[int]   					=   x
							else:
												UpperCAmelCase		:							int   					=   x







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							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    ,			snake_case    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							Any   					=   ()
							UpperCAmelCase		:							Optional[int]   					=   ()
							UpperCAmelCase		:							Optional[int]   					=   ()
							for i, layer_module in enumerate(self.layer			):
												if self.output_hidden_states:
																	UpperCAmelCase		:							List[str]   					=   all_hidden_states + (hidden_states,)

												UpperCAmelCase		:							List[Any]   					=   layer_module(
												    UpperCAmelCase_    ,			UpperCAmelCase_    ,			head_mask[i]    ,			UpperCAmelCase_    ,			UpperCAmelCase_			)
												UpperCAmelCase		:							Optional[Any]   					=   layer_outputs[0]

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

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

												UpperCAmelCase		:							List[str]   					=   self.highway[i](UpperCAmelCase_			)
												# logits, pooled_output

												if not self.training:
																	UpperCAmelCase		:							List[Any]   					=   highway_exit[0]
																	UpperCAmelCase		:							List[str]   					=   entropy(UpperCAmelCase_			)
																	UpperCAmelCase		:							str   					=   highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																	UpperCAmelCase		:							int   					=   all_highway_exits + (highway_exit,)

																	if highway_entropy < self.early_exit_entropy[i]:
																						UpperCAmelCase		:							Dict   					=   (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																						raise HighwayException(UpperCAmelCase_    ,			i + 1			)
												else:
																	UpperCAmelCase		:							Optional[int]   					=   all_highway_exits + (highway_exit,)

																	 # Add last layer
							if self.output_hidden_states:
												UpperCAmelCase		:							Union[str, Any]   					=   all_hidden_states + (hidden_states,)

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

							UpperCAmelCase		:							Any   					=   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   UpperCamelCase__    (   __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case			):


							'''simple docstring'''

							super().__init__(UpperCAmelCase_			)
							UpperCAmelCase		:							Any   					=   config

							UpperCAmelCase		:							Tuple   					=   BertEmbeddings(UpperCAmelCase_			)
							UpperCAmelCase		:							str   					=   DeeBertEncoder(UpperCAmelCase_			)
							UpperCAmelCase		:							int   					=   BertPooler(UpperCAmelCase_			)

							self.init_weights()







		def       A_       ( self			):


							'''simple docstring'''

							self.encoder.init_highway_pooler(self.pooler			)







		def       A_       ( self			):


							'''simple docstring'''

							return self.embeddings.word_embeddings







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							str   					=   value







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

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







		@add_start_docstrings_to_model_forward(UpperCAmelCase_			)
		def       A_       ( self    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			):


							'''simple docstring'''

							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:
												UpperCAmelCase		:							Union[str, Any]   					=   input_ids.size()
							elif inputs_embeds is not None:
												UpperCAmelCase		:							Optional[int]   					=   inputs_embeds.size()[:-1]
							else:
												raise ValueError("You have to specify either input_ids or inputs_embeds"			)

							UpperCAmelCase		:							Optional[Any]   					=   input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
												UpperCAmelCase		:							int   					=   torch.ones(UpperCAmelCase_    ,			device=UpperCAmelCase_			)
							if encoder_attention_mask is None:
												UpperCAmelCase		:							List[Any]   					=   torch.ones(UpperCAmelCase_    ,			device=UpperCAmelCase_			)
							if token_type_ids is None:
												UpperCAmelCase		:							Dict   					=   torch.zeros(UpperCAmelCase_    ,			dtype=torch.long    ,			device=UpperCAmelCase_			)

							# 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.
							UpperCAmelCase		:							torch.Tensor   					=   self.get_extended_attention_mask(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_			)

							# 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:
												UpperCAmelCase		:							Dict   					=   encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
												UpperCAmelCase		:							Optional[int]   					=   encoder_attention_mask[:, None, None, :]

							UpperCAmelCase		:							List[str]   					=   encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()			).dtype			)  # fp16 compatibility
							UpperCAmelCase		:							List[str]   					=   (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]
							UpperCAmelCase		:							Optional[Any]   					=   self.get_head_mask(UpperCAmelCase_    ,			self.config.num_hidden_layers			)

							UpperCAmelCase		:							int   					=   self.embeddings(
							    input_ids=UpperCAmelCase_    ,			position_ids=UpperCAmelCase_    ,			token_type_ids=UpperCAmelCase_    ,			inputs_embeds=UpperCAmelCase_			)
							UpperCAmelCase		:							List[Any]   					=   self.encoder(
							    UpperCAmelCase_    ,			attention_mask=UpperCAmelCase_    ,			head_mask=UpperCAmelCase_    ,			encoder_hidden_states=UpperCAmelCase_    ,			encoder_attention_mask=UpperCAmelCase_    ,			)
							UpperCAmelCase		:							List[str]   					=   encoder_outputs[0]
							UpperCAmelCase		:							Optional[Any]   					=   self.pooler(UpperCAmelCase_			)

							UpperCAmelCase		:							Optional[int]   					=   (
							    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   UpperCamelCase__    (   __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Any   					=   message
							UpperCAmelCase		:							int   					=   exit_layer  # start from 1!







class   UpperCamelCase__    (   nn.Module   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case			):


							'''simple docstring'''

							super().__init__()
							UpperCAmelCase		:							Tuple   					=   BertPooler(UpperCAmelCase_			)
							UpperCAmelCase		:							Optional[int]   					=   nn.Dropout(config.hidden_dropout_prob			)
							UpperCAmelCase		:							int   					=   nn.Linear(config.hidden_size    ,			config.num_labels			)







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple   					=   encoder_outputs[0]
							UpperCAmelCase		:							Optional[int]   					=   self.pooler(UpperCAmelCase_			)
							# "return" pooler_output

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

							# Dropout and classification
							UpperCAmelCase		:							int   					=   bmodel_output[1]

							UpperCAmelCase		:							Union[str, Any]   					=   self.dropout(UpperCAmelCase_			)
							UpperCAmelCase		:							int   					=   self.classifier(UpperCAmelCase_			)

							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   UpperCamelCase__    (   __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case			):


							'''simple docstring'''

							super().__init__(UpperCAmelCase_			)
							UpperCAmelCase		:							Optional[Any]   					=   config.num_labels
							UpperCAmelCase		:							Tuple   					=   config.num_hidden_layers

							UpperCAmelCase		:							Dict   					=   DeeBertModel(UpperCAmelCase_			)
							UpperCAmelCase		:							Any   					=   nn.Dropout(config.hidden_dropout_prob			)
							UpperCAmelCase		:							List[Any]   					=   nn.Linear(config.hidden_size    ,			self.config.num_labels			)

							self.init_weights()







		@add_start_docstrings_to_model_forward(UpperCAmelCase_			)
		def       A_       ( self    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=None    ,			snake_case=-1    ,			snake_case=False    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							Dict   					=   self.num_layers
							try:
												UpperCAmelCase		:							str   					=   self.bert(
												    UpperCAmelCase_    ,			attention_mask=UpperCAmelCase_    ,			token_type_ids=UpperCAmelCase_    ,			position_ids=UpperCAmelCase_    ,			head_mask=UpperCAmelCase_    ,			inputs_embeds=UpperCAmelCase_    ,			)
												# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

												UpperCAmelCase		:							Union[str, Any]   					=   outputs[1]

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

							if not self.training:
												UpperCAmelCase		:							Optional[Any]   					=   entropy(UpperCAmelCase_			)
												UpperCAmelCase		:							str   					=   []
												UpperCAmelCase		:							List[str]   					=   []
							if labels is not None:
												if self.num_labels == 1:
																	#  We are doing regression
																	UpperCAmelCase		:							Optional[Any]   					=   MSELoss()
																	UpperCAmelCase		:							Any   					=   loss_fct(logits.view(-1			)    ,			labels.view(-1			)			)
												else:
																	UpperCAmelCase		:							str   					=   CrossEntropyLoss()
																	UpperCAmelCase		:							Union[str, Any]   					=   loss_fct(logits.view(-1    ,			self.num_labels			)    ,			labels.view(-1			)			)

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

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







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
     __A									=				"platform"

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )





def      lowerCAmelCase_ (       __a	,    __a	,    __a=None	,    __a=None	,    __a=None	,    __a=None	,    __a=None	,    __a=None	,    )     ->		Any:







  """simple docstring"""





  if attention_mask is None:
    lowerCamelCase__:      Optional[Any]			  =np.where(input_ids != config.pad_token_id	,    1	,    0		)
  if decoder_attention_mask is None:
    lowerCamelCase__:      Dict			  =np.where(decoder_input_ids != config.pad_token_id	,    1	,    0		)
  if head_mask is None:
    lowerCamelCase__:      Optional[Any]			  =np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    lowerCamelCase__:      Any			  =np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    lowerCamelCase__:      List[str]			  =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    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Tuple       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Dict=13       , UpperCAmelCase_				:   List[Any]=7       , UpperCAmelCase_				:   str=True       , UpperCAmelCase_				:   Optional[int]=False       , UpperCAmelCase_				:   Union[str, Any]=99       , UpperCAmelCase_				:   Any=16       , UpperCAmelCase_				:   Dict=2       , UpperCAmelCase_				:   Any=4       , UpperCAmelCase_				:   List[Any]=4       , UpperCAmelCase_				:   int="gelu"       , UpperCAmelCase_				:   int=0.1       , UpperCAmelCase_				:   str=0.1       , UpperCAmelCase_				:   Tuple=32       , UpperCAmelCase_				:   int=2       , UpperCAmelCase_				:   int=1       , UpperCAmelCase_				:   Union[str, Any]=0       , UpperCAmelCase_				:   Any=0.02       , )       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      int			  =parent
   lowerCamelCase__:      List[str]			  =batch_size
   lowerCamelCase__:      Optional[int]			  =seq_length
   lowerCamelCase__:      Optional[Any]			  =is_training
   lowerCamelCase__:      str			  =use_labels
   lowerCamelCase__:      Optional[Any]			  =vocab_size
   lowerCamelCase__:      int			  =hidden_size
   lowerCamelCase__:      Dict			  =num_hidden_layers
   lowerCamelCase__:      Any			  =num_attention_heads
   lowerCamelCase__:      str			  =intermediate_size
   lowerCamelCase__:      int			  =hidden_act
   lowerCamelCase__:      Tuple			  =hidden_dropout_prob
   lowerCamelCase__:      List[str]			  =attention_probs_dropout_prob
   lowerCamelCase__:      Optional[int]			  =max_position_embeddings
   lowerCamelCase__:      int			  =eos_token_id
   lowerCamelCase__:      Union[str, Any]			  =pad_token_id
   lowerCamelCase__:      List[str]			  =bos_token_id
   lowerCamelCase__:      int			  =initializer_range

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       , self.vocab_size)       , 3       , self.vocab_size)
   lowerCamelCase__:      str			  =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       , dtype=np.intaa))       , -1)

   lowerCamelCase__:      int			  =shift_tokens_right(UpperCAmelCase_       , 1       , 2)

   lowerCamelCase__:      Dict			  =BlenderbotConfig(
       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=UpperCAmelCase_       , )
   lowerCamelCase__:      Any			  =prepare_blenderbot_inputs_dict(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
   return config, inputs_dict

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      Dict			  =self.prepare_config_and_inputs()
   return config, inputs_dict

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =20
   lowerCamelCase__:      Optional[int]			  =model_class_name(UpperCAmelCase_)

   lowerCamelCase__:      str			  =model.encode(inputs_dict["input_ids"])

   lowerCamelCase__  ,					lowerCamelCase__:      List[Any]			  =(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   lowerCamelCase__:      Union[str, Any]			  =model.init_cache(decoder_input_ids.shape[0]       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       , dtype="i4")

   lowerCamelCase__:      Tuple			  =jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]       , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       , )
   lowerCamelCase__:      Union[str, Any]			  =model.decode(
       decoder_input_ids[:, :-1]       , UpperCAmelCase_       , decoder_attention_mask=UpperCAmelCase_       , past_key_values=UpperCAmelCase_       , decoder_position_ids=UpperCAmelCase_       , )

   lowerCamelCase__:      Union[str, Any]			  =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype="i4")
   lowerCamelCase__:      Dict			  =model.decode(
       decoder_input_ids[:, -1:]       , UpperCAmelCase_       , decoder_attention_mask=UpperCAmelCase_       , past_key_values=outputs_cache.past_key_values       , decoder_position_ids=UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =model.decode(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =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 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =20
   lowerCamelCase__:      Optional[Any]			  =model_class_name(UpperCAmelCase_)

   lowerCamelCase__:      Any			  =model.encode(inputs_dict["input_ids"])

   lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   lowerCamelCase__:      Optional[int]			  =jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
       ]       , axis=-1       , )

   lowerCamelCase__:      Union[str, Any]			  =model.init_cache(decoder_input_ids.shape[0]       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]       , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       , )

   lowerCamelCase__:      List[Any]			  =model.decode(
       decoder_input_ids[:, :-1]       , UpperCAmelCase_       , decoder_attention_mask=UpperCAmelCase_       , past_key_values=UpperCAmelCase_       , decoder_position_ids=UpperCAmelCase_       , )
   lowerCamelCase__:      Dict			  =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype="i4")
   lowerCamelCase__:      str			  =model.decode(
       decoder_input_ids[:, -1:]       , UpperCAmelCase_       , past_key_values=outputs_cache.past_key_values       , decoder_attention_mask=UpperCAmelCase_       , decoder_position_ids=UpperCAmelCase_       , )

   lowerCamelCase__:      Union[str, Any]			  =model.decode(UpperCAmelCase_       , UpperCAmelCase_       , decoder_attention_mask=UpperCAmelCase_)

   lowerCamelCase__:      str			  =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    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			99

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =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       , )

   lowerCamelCase__:      Optional[Any]			  =input_ids.shape[0]
   lowerCamelCase__:      List[str]			  =BlenderbotConfig(
       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 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Any			  =self._get_config_and_data()
   lowerCamelCase__:      Dict			  =FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =lm_model(input_ids=UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =(batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["logits"].shape       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =BlenderbotConfig(
       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       , )
   lowerCamelCase__:      str			  =FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       , dtype=np.intaa)
   lowerCamelCase__:      Optional[int]			  =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       , dtype=np.intaa)
   lowerCamelCase__:      List[str]			  =lm_model(input_ids=UpperCAmelCase_       , decoder_input_ids=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =(*summary.shape, config.vocab_size)
   self.assertEqual(outputs["logits"].shape       , UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



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



@require_flax
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ,							__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			True
 lowercase_		=			(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase_		=			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =FlaxBlenderbotModelTester(self)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      List[str]			  =self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      List[str]			  =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(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowerCamelCase__:      List[str]			  =self._prepare_for_class(UpperCAmelCase_       , UpperCAmelCase_)
       lowerCamelCase__:      Optional[int]			  =model_class(UpperCAmelCase_)

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

       with self.subTest("JIT Enabled"):
         lowerCamelCase__:      Any			  =encode_jitted(**UpperCAmelCase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           lowerCamelCase__:      Tuple			  =encode_jitted(**UpperCAmelCase_).to_tuple()

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      List[Any]			  =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowerCamelCase__:      Optional[Any]			  =model_class(UpperCAmelCase_)
       lowerCamelCase__:      List[Any]			  =model.encode(inputs_dict["input_ids"]       , inputs_dict["attention_mask"])

       lowerCamelCase__:      int			  ={
           "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(UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[int]):
         return model.decode(
             decoder_input_ids=UpperCAmelCase_       , decoder_attention_mask=UpperCAmelCase_       , encoder_outputs=UpperCAmelCase_       , )

       with self.subTest("JIT Enabled"):
         lowerCamelCase__:      int			  =decode_jitted(**UpperCAmelCase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           lowerCamelCase__:      int			  =decode_jitted(**UpperCAmelCase_).to_tuple()

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

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Union[str, Any]:



   '''simple docstring'''



   for model_class_name in self.all_model_classes:
     lowerCamelCase__:      Optional[int]			  =model_class_name.from_pretrained("facebook/blenderbot-400M-distill")
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     lowerCamelCase__:      int			  =np.ones((1, 1)) * model.config.eos_token_id
     lowerCamelCase__:      str			  =model(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)







 @unittest.skipUnless(jax_device != "cpu"       , "3B test too slow on CPU.")
 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ={"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
   lowerCamelCase__:      Union[str, Any]			  ={"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

   lowerCamelCase__:      Dict			  =FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"       , from_pt=UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")

   lowerCamelCase__:      Any			  =["Sam"]
   lowerCamelCase__:      Tuple			  =tokenizer(UpperCAmelCase_       , return_tensors="jax")

   lowerCamelCase__:      Optional[Any]			  =model.generate(**UpperCAmelCase_       , **UpperCAmelCase_)
   lowerCamelCase__:      Any			  ="Sam is a great name. It means \"sun\" in Gaelic."

   lowerCamelCase__:      Optional[Any]			  =tokenizer.batch_decode(UpperCAmelCase_       , **UpperCAmelCase_)
   assert generated_txt[0].strip() == tgt_text

