



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    tempfile.mkdtemp()

														_lowerCAmelCase     :							List[Any]    =    [
														    "[UNK]",
														    "[CLS]",
														    "[SEP]",
														    "[PAD]",
														    "[MASK]",
														    "的",
														    "价",
														    "格",
														    "是",
														    "15",
														    "便",
														    "alex",
														    "##andra",
														    "，",
														    "。",
														    "-",
														    "t",
														    "shirt",
														]
														_lowerCAmelCase     :							Tuple    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"])
														with open(self.vocab_file,			"w",			encoding="utf-8") as vocab_writer:
																					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

														_lowerCAmelCase     :							str    =    {
														    "do_resize": True,
														    "size": {"height": 224, "width": 224},
														    "do_center_crop": True,
														    "crop_size": {"height": 18, "width": 18},
														    "do_normalize": True,
														    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
														    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
														    "do_convert_rgb": True,
														}
														_lowerCAmelCase     :							Union[str, Any]    =    os.path.join(self.tmpdirname,			__a)
														with open(self.image_processor_file,			"w",			encoding="utf-8") as fp:
																					json.dump(__a,			__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return BertTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return BertTokenizerFast.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														shutil.rmtree(self.tmpdirname)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    [np.random.randint(255,			size=(3, 30, 400),			dtype=np.uinta)]

														_lowerCAmelCase     :							Any    =    [Image.fromarray(np.moveaxis(__a,			0,			-1)) for x in image_inputs]

														return image_inputs







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    self.get_rust_tokenizer()
														_lowerCAmelCase     :							Optional[int]    =    self.get_image_processor()

														_lowerCAmelCase     :							Tuple    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)
														processor_slow.save_pretrained(self.tmpdirname)
														_lowerCAmelCase     :							Optional[Any]    =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=__a)

														_lowerCAmelCase     :							Tuple    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)
														processor_fast.save_pretrained(self.tmpdirname)
														_lowerCAmelCase     :							Tuple    =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname)

														self.assertEqual(processor_slow.tokenizer.get_vocab(),			tokenizer_slow.get_vocab())
														self.assertEqual(processor_fast.tokenizer.get_vocab(),			tokenizer_fast.get_vocab())
														self.assertEqual(tokenizer_slow.get_vocab(),			tokenizer_fast.get_vocab())
														self.assertIsInstance(processor_slow.tokenizer,			__a)
														self.assertIsInstance(processor_fast.tokenizer,			__a)

														self.assertEqual(processor_slow.image_processor.to_json_string(),			image_processor.to_json_string())
														self.assertEqual(processor_fast.image_processor.to_json_string(),			image_processor.to_json_string())
														self.assertIsInstance(processor_slow.image_processor,			__a)
														self.assertIsInstance(processor_fast.image_processor,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor())
														processor.save_pretrained(self.tmpdirname)

														_lowerCAmelCase     :							int    =    self.get_tokenizer(cls_token="(CLS)",			sep_token="(SEP)")
														_lowerCAmelCase     :							Dict    =    self.get_image_processor(do_normalize=__a)

														_lowerCAmelCase     :							Tuple    =    ChineseCLIPProcessor.from_pretrained(
														    self.tmpdirname,			cls_token="(CLS)",			sep_token="(SEP)",			do_normalize=__a)

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

														self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string())
														self.assertIsInstance(processor.image_processor,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    self.get_image_processor()
														_lowerCAmelCase     :							List[Any]    =    self.get_tokenizer()

														_lowerCAmelCase     :							List[Any]    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)

														_lowerCAmelCase     :							List[str]    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							str    =    image_processor(__a,			return_tensors="np")
														_lowerCAmelCase     :							Union[str, Any]    =    processor(images=__a,			return_tensors="np")

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1E-2)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.get_image_processor()
														_lowerCAmelCase     :							int    =    self.get_tokenizer()

														_lowerCAmelCase     :							Optional[int]    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)

														_lowerCAmelCase     :							Optional[Any]    =    "Alexandra，T-shirt的价格是15便士。"

														_lowerCAmelCase     :							Union[str, Any]    =    processor(text=__a)

														_lowerCAmelCase     :							str    =    tokenizer(__a)

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.get_image_processor()
														_lowerCAmelCase     :							int    =    self.get_tokenizer()

														_lowerCAmelCase     :							Dict    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)

														_lowerCAmelCase     :							int    =    "Alexandra，T-shirt的价格是15便士。"
														_lowerCAmelCase     :							Any    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							Dict    =    processor(text=__a,			images=__a)

														self.assertListEqual(list(inputs.keys()),			["input_ids", "token_type_ids", "attention_mask", "pixel_values"])

														# test if it raises when no input is passed
														with pytest.raises(__a):
																					processor()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.get_image_processor()
														_lowerCAmelCase     :							List[str]    =    self.get_tokenizer()

														_lowerCAmelCase     :							Union[str, Any]    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)

														_lowerCAmelCase     :							Tuple    =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														_lowerCAmelCase     :							Union[str, Any]    =    processor.batch_decode(__a)
														_lowerCAmelCase     :							Tuple    =    tokenizer.batch_decode(__a)

														self.assertListEqual(__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.get_image_processor()
														_lowerCAmelCase     :							Dict    =    self.get_tokenizer()

														_lowerCAmelCase     :							Dict    =    ChineseCLIPProcessor(tokenizer=__a,			image_processor=__a)

														_lowerCAmelCase     :							Any    =    "Alexandra，T-shirt的价格是15便士。"
														_lowerCAmelCase     :							Optional[Any]    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							Any    =    processor(text=__a,			images=__a)

														self.assertListEqual(list(inputs.keys()),			processor.model_input_names)





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
