



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


_snake_case		=  ["gpt2"]
_snake_case		=  "gpt2"

if is_tf_available():



						class   UpperCAmelCase_    (       tf.Module):







													def __init__(   self,			__a):

																				'''simple docstring'''

																				super().__init__()
																				_lowerCAmelCase     :							Tuple    =    tokenizer
																				_lowerCAmelCase     :							int    =    AutoConfig.from_pretrained(__a)
																				_lowerCAmelCase     :							Optional[int]    =    TFGPTaLMHeadModel.from_config(__a)







													@tf.function(input_signature=(tf.TensorSpec((None,),			tf.string,			name="text"),))
													def    snake_case__      (   self,			__a):

																				'''simple docstring'''

																				_lowerCAmelCase     :							Dict    =    self.tokenizer(__a)
																				_lowerCAmelCase     :							Optional[int]    =    tokenized["input_ids"].to_tensor()

																				_lowerCAmelCase     :							str    =    tf.cast(input_ids_dense > 0,			tf.intaa)
																				# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																				_lowerCAmelCase     :							Optional[int]    =    self.model(input_ids=__a,			attention_mask=__a)["logits"]

																				return outputs



@require_tf
@require_keras_nlp
class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							Tuple    =    [GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)]
														_lowerCAmelCase     :							str    =    [TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS]
														assert len(self.tokenizers) == len(self.tf_tokenizers)

														_lowerCAmelCase     :							Optional[Any]    =    [
														    "This is a straightforward English test sentence.",
														    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
														    "Now we're going to add some Chinese: 一 二 三 一二三",
														    "And some much more rare Chinese: 齉 堃 齉堃",
														    "Je vais aussi écrire en français pour tester les accents",
														    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
														]
														_lowerCAmelCase     :							int    =    list(zip(self.test_sentences,			self.test_sentences[::-1]))







							def    snake_case__      (   self):

														'''simple docstring'''

														for tokenizer, tf_tokenizer in zip(self.tokenizers,			self.tf_tokenizers):
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase     :							Union[str, Any]    =    tokenizer([test_inputs],			return_tensors="tf")
																												_lowerCAmelCase     :							Dict    =    tf_tokenizer([test_inputs])

																												for key in python_outputs.keys():
																																			# convert them to numpy to avoid messing with ragged tensors
																																			_lowerCAmelCase     :							Tuple    =    python_outputs[key].numpy()
																																			_lowerCAmelCase     :							List[str]    =    tf_outputs[key].numpy()

																																			self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
																																			self.assertTrue(tf.reduce_all(tf.cast(__a,			tf.intaa) == tf_outputs_values))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							str    =    tf.function(__a)
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase     :							Any    =    tf.constant(__a)
																												_lowerCAmelCase     :							int    =    compiled_tokenizer(__a)
																												_lowerCAmelCase     :							int    =    tf_tokenizer(__a)

																												for key in eager_outputs.keys():
																																			self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							List[str]    =    ModelToSave(tokenizer=__a)
																					_lowerCAmelCase     :							int    =    tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase     :							str    =    model.serving(__a)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												_lowerCAmelCase     :							Optional[Any]    =    Path(__a) / "saved.model"
																												tf.saved_model.save(__a,			__a,			signatures={"serving_default": model.serving})
																												_lowerCAmelCase     :							Optional[Any]    =    tf.saved_model.load(__a)
																					_lowerCAmelCase     :							Union[str, Any]    =    loaded_model.signatures["serving_default"](__a)["output_0"]
																					# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																					self.assertTrue(tf.reduce_all(out == loaded_output))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							Optional[int]    =    tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase     :							Optional[Any]    =    tf_tokenizer(__a)  # Build model with some sample inputs

																					_lowerCAmelCase     :							Optional[Any]    =    tf_tokenizer.get_config()
																					_lowerCAmelCase     :							Optional[Any]    =    TFGPTaTokenizer.from_config(__a)
																					_lowerCAmelCase     :							Tuple    =    model_from_config(__a)

																					for key in from_config_output.keys():
																												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					_lowerCAmelCase     :							Any    =    12_3123

																					for max_length in [3, 5, 1024]:
																												_lowerCAmelCase     :							Optional[Any]    =    tf.convert_to_tensor([self.test_sentences[0]])
																												_lowerCAmelCase     :							Optional[Any]    =    tf_tokenizer(__a,			max_length=__a)

																												_lowerCAmelCase     :							Optional[Any]    =    out["input_ids"].numpy().shape[1]

																												assert out_length == max_length





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). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



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







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

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

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

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    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[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

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