
'''simple docstring'''







import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import BertGenerationDecoder, BertGenerationEncoder


class 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=7					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=99					,						UpperCamelCase=32					,						UpperCamelCase=5					,						UpperCamelCase=4					,						UpperCamelCase=37					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=50					,						UpperCamelCase=0.02					,						UpperCamelCase=True					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= seq_length
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= use_input_mask
								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= scope




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size      )

								_SCREAMING_SNAKE_CASE				= None
								if self.use_input_mask:
														_SCREAMING_SNAKE_CASE				= random_attention_mask([self.batch_size, self.seq_length]      )

								if self.use_labels:
														_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size      )

								_SCREAMING_SNAKE_CASE				= self.get_config()

								return config, input_ids, input_mask, token_labels




		def     lowercase       (				self      ):
								return BertGenerationConfig(
								    vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						is_decoder=UpperCamelCase					,						initializer_range=self.initializer_range					,						)




		def     lowercase       (				self      ):
								(
								    (
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							
								)				= self.prepare_config_and_inputs()

								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
								_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2      )

								return (
								    config,
								    input_ids,
								    input_mask,
								    token_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= BertGenerationEncoder(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= BertGenerationEncoder(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						encoder_attention_mask=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						)
								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= BertGenerationDecoder(config=UpperCamelCase      ).to(UpperCamelCase      ).eval()

								# first forward pass
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						encoder_attention_mask=UpperCamelCase					,						use_cache=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								_SCREAMING_SNAKE_CASE				= ids_tensor((self.batch_size, 3)					,						config.vocab_size      )
								_SCREAMING_SNAKE_CASE				= ids_tensor((self.batch_size, 3)					,						vocab_size=2      )

								# append to next input_ids and
								_SCREAMING_SNAKE_CASE				= torch.cat([input_ids, next_tokens]					,						dim=-1      )
								_SCREAMING_SNAKE_CASE				= torch.cat([input_mask, next_mask]					,						dim=-1      )

								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						encoder_attention_mask=UpperCamelCase					,						output_hidden_states=UpperCamelCase					,						)["hidden_states"][0]
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						encoder_attention_mask=UpperCamelCase					,						past_key_values=UpperCamelCase					,						output_hidden_states=UpperCamelCase					,						)["hidden_states"][0]

								# select random slice
								_SCREAMING_SNAKE_CASE				= ids_tensor((1,)					,						output_from_past.shape[-1]      ).item()
								_SCREAMING_SNAKE_CASE				= output_from_no_past[:, -3:, random_slice_idx].detach()
								_SCREAMING_SNAKE_CASE				= output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(UpperCamelCase					,						UpperCamelCase					,						atol=1e-3      )      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						*UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= BertGenerationDecoder(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.prepare_config_and_inputs()
								_SCREAMING_SNAKE_CASE				= {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Tuple       =					(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
		a							:					Optional[int]       =					(BertGenerationDecoder,) if is_torch_available() else ()
		a							:					Union[str, Any]       =					(
		    {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
		    if is_torch_available()
		    else {}
		)




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BertGenerationEncoderTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase					,						hidden_size=37      )




		def     lowercase       (				self      ):
								self.config_tester.run_common_tests()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								_SCREAMING_SNAKE_CASE				= "bert"
								self.model_tester.create_and_check_model(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase      )




		def     lowercase       (				self      ):
								# This regression test was failing with PyTorch < 1.3
								(
								    (
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							
								)				= self.model_tester.prepare_config_and_inputs_for_decoder()

								_SCREAMING_SNAKE_CASE				= None

								self.model_tester.create_and_check_model_as_decoder(
								    UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						)




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase      )




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )
								self.assertIsNotNone(UpperCamelCase      )


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )
								_SCREAMING_SNAKE_CASE				= torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]      )
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )[0]
								_SCREAMING_SNAKE_CASE				= torch.Size([1, 8, 1_024]      )
								self.assertEqual(output.shape					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= torch.tensor(
								    [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]      )
								self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase					,						atol=1e-4      )      )


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )
								_SCREAMING_SNAKE_CASE				= torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]      )
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )[0]
								_SCREAMING_SNAKE_CASE				= torch.Size([1, 8, 50_358]      )
								self.assertEqual(output.shape					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= torch.tensor(
								    [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]      )
								self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase					,						atol=1e-4      )      )

'''simple docstring'''







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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

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

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

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




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




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )