





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class 	A_       (a_			):
	UpperCAmelCase__					=			['''image_processor''', '''tokenizer''']
	UpperCAmelCase__					=			'''BlipImageProcessor'''
	UpperCAmelCase__					=			'''AutoTokenizer'''
	def __init__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__(_A							,					_A		)

				# add QFormer tokenizer
				UpperCAmelCase   		=				qformer_tokenizer
	def __call__(    self							,					_A = None							,					_A = None							,					_A = True							,					_A = False							,					_A = None							,					_A = None							,					_A = 0							,					_A = None							,					_A = None							,					_A = False							,					_A = False							,					_A = False							,					_A = False							,					_A = False							,					_A = True							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				if images is None and text is None:
							raise ValueError('''You have to specify at least images or text.'''		)

				UpperCAmelCase   		=				BatchFeature()

				if text is not None:
							UpperCAmelCase   		=				self.tokenizer(
							    text=_A							,					add_special_tokens=_A							,					padding=_A							,					truncation=_A							,					max_length=_A							,					stride=_A							,					pad_to_multiple_of=_A							,					return_attention_mask=_A							,					return_overflowing_tokens=_A							,					return_special_tokens_mask=_A							,					return_offsets_mapping=_A							,					return_token_type_ids=_A							,					return_length=_A							,					verbose=_A							,					return_tensors=_A							,					**_A							,					)
							encoding.update(_A		)
							UpperCAmelCase   		=				self.qformer_tokenizer(
							    text=_A							,					add_special_tokens=_A							,					padding=_A							,					truncation=_A							,					max_length=_A							,					stride=_A							,					pad_to_multiple_of=_A							,					return_attention_mask=_A							,					return_overflowing_tokens=_A							,					return_special_tokens_mask=_A							,					return_offsets_mapping=_A							,					return_token_type_ids=_A							,					return_length=_A							,					verbose=_A							,					return_tensors=_A							,					**_A							,					)
							UpperCAmelCase   		=				qformer_text_encoding.pop('''input_ids'''		)
							UpperCAmelCase   		=				qformer_text_encoding.pop('''attention_mask'''		)

				if images is not None:
							UpperCAmelCase   		=				self.image_processor(_A							,					return_tensors=_A		)
							encoding.update(_A		)

				return encoding
	def      _lowercase				(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				return self.tokenizer.batch_decode(*_A							,					**_A		)
	def      _lowercase				(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				return self.tokenizer.decode(*_A							,					**_A		)
	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.tokenizer.model_input_names
				UpperCAmelCase   		=				self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
	def      _lowercase				(    self							,					_A							,					**_A		):






				'''simple docstring'''







				if os.path.isfile(_A		):
							raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""		)
				os.makedirs(_A							,					exist_ok=_A		)
				UpperCAmelCase   		=				os.path.join(_A							,					'''qformer_tokenizer'''		)
				self.qformer_tokenizer.save_pretrained(_A		)
				return super().save_pretrained(_A							,					**_A		)







	@classmethod
	def      _lowercase				(    cls							,					_A							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				AutoTokenizer.from_pretrained(_A							,					subfolder='''qformer_tokenizer'''		)
				UpperCAmelCase   		=				cls._get_arguments_from_pretrained(_A							,					**_A		)
				args.append(_A		)
				return cls(*_A		)










import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
				import torch

				from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	A_       (unittest.TestCase			):
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)



