'''simple docstring'''




from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy





@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''',  _UpperCAmelCase,  )
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RobertaConfig
			UpperCamelCase__       = '''roberta'''







			def __init__(			self       :						List[str]       ,    lowercase_       :						Optional[int]					):
										super().__init__(lowercase_					)

										lowercase_      :    int       		= RobertaEmbeddings(lowercase_					)
										self.init_weights()





@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. ''',  _UpperCAmelCase,  )
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RobertaConfig
			UpperCamelCase__       = '''roberta'''







			def __init__(			self       :						List[str]       ,    lowercase_       :						int					):
										super().__init__(lowercase_					)
										lowercase_      :    str       		= config.num_labels
										lowercase_      :    List[str]       		= config.num_hidden_layers

										lowercase_      :    Dict       		= DeeRobertaModel(lowercase_					)
										lowercase_      :    Union[str, Any]       		= nn.Dropout(config.hidden_dropout_prob					)
										lowercase_      :    Optional[Any]       		= nn.Linear(config.hidden_size       ,    self.config.num_labels					)







			@add_start_docstrings_to_model_forward(lowercase_					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Dict=None       ,    lowercase_       :						int=None       ,    lowercase_       :						Optional[int]=None       ,    lowercase_       :						List[str]=None       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						int=None       ,    lowercase_       :						List[str]=None       ,    lowercase_       :						List[str]=-1       ,    lowercase_       :						Tuple=False       ,    ):
										lowercase_      :    int       		= self.num_layers
										try:
																	lowercase_      :    List[str]       		= self.roberta(
																	    lowercase_       ,    attention_mask=lowercase_       ,    token_type_ids=lowercase_       ,    position_ids=lowercase_       ,    head_mask=lowercase_       ,    inputs_embeds=lowercase_       ,    )

																	lowercase_      :    Optional[Any]       		= outputs[1]

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

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

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

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

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






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
										lowercase_      :    List[str]       		= tempfile.mkdtemp()
										lowercase_      :    Tuple       		= """en_speaker_1"""
										lowercase_      :    Union[str, Any]       		= """This is a test string"""
										lowercase_      :    int       		= """speaker_embeddings_path.json"""
										lowercase_      :    Any       		= """speaker_embeddings"""







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
										return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )
										processor.save_pretrained(
										    self.tmpdirname       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    speaker_embeddings_directory=self.speaker_embeddings_directory       ,    )

										lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

										lowercase_      :    Optional[Any]       		= BarkProcessor.from_pretrained(
										    self.tmpdirname       ,    self.speaker_embeddings_dict_path       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    )

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )

										lowercase_      :    Optional[int]       		= 35
										lowercase_      :    int       		= 2
										lowercase_      :    Union[str, Any]       		= 8

										lowercase_      :    Union[str, Any]       		= {
										    """semantic_prompt""": np.ones(lowercase_					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

										lowercase_      :    Dict       		= inputs["""history_prompt"""]
										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from npz file
										lowercase_      :    Any       		= os.path.join(self.tmpdirname       ,    """file.npz"""					)
										np.savez(lowercase_       ,    **lowercase_					)
										lowercase_      :    Optional[Any]       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)
										lowercase_      :    List[Any]       		= inputs["""history_prompt"""]

										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from the hub
										lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.get_tokenizer()

										lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

										lowercase_      :    Any       		= processor(text=self.input_string					)

										lowercase_      :    List[str]       		= tokenizer(
										    self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key].squeeze().tolist()					)





