





'''simple docstring'''






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)
@require_vision
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   tempfile.mkdtemp()

			lowerCAmelCase     :    Tuple						=   BlipImageProcessor()
			lowerCAmelCase     :    Any						=   GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"	)
			lowerCAmelCase     :    Any						=   BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"	)

			lowerCAmelCase     :    str						=   InstructBlipProcessor(a_		,      a_		,      a_	)

			processor.save_pretrained(self.tmpdirname	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return AutoProcessor.from_pretrained(self.tmpdirname		,      **a_	).tokenizer



	def   _lowerCamelCase    (   self		,      **a_	):
			return AutoProcessor.from_pretrained(self.tmpdirname		,      **a_	).image_processor



	def   _lowerCamelCase    (   self		,      **a_	):
			return AutoProcessor.from_pretrained(self.tmpdirname		,      **a_	).qformer_tokenizer



	def   _lowerCamelCase    (   self	):
			shutil.rmtree(self.tmpdirname	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   [np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta	)]

			lowerCAmelCase     :    Dict						=   [Image.fromarray(np.moveaxis(a_		,      0		,      -1	)	) for x in image_inputs]

			return image_inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   InstructBlipProcessor(
			    tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()		,      qformer_tokenizer=self.get_qformer_tokenizer()		,      )
			processor.save_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    str						=   self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"	)
			lowerCAmelCase     :    str						=   self.get_image_processor(do_normalize=a_		,      padding_value=1.0	)

			lowerCAmelCase     :    str						=   InstructBlipProcessor.from_pretrained(
			    self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=a_		,      padding_value=1.0	)

			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_	)
			self.assertIsInstance(processor.qformer_tokenizer		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.get_image_processor()
			lowerCAmelCase     :    int						=   self.get_tokenizer()
			lowerCAmelCase     :    Optional[Any]						=   self.get_qformer_tokenizer()

			lowerCAmelCase     :    Union[str, Any]						=   InstructBlipProcessor(
			    tokenizer=a_		,      image_processor=a_		,      qformer_tokenizer=a_	)

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

			lowerCAmelCase     :    str						=   image_processor(a_		,      return_tensors="np"	)
			lowerCAmelCase     :    Optional[int]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.get_image_processor()
			lowerCAmelCase     :    str						=   self.get_tokenizer()
			lowerCAmelCase     :    Dict						=   self.get_qformer_tokenizer()

			lowerCAmelCase     :    List[Any]						=   InstructBlipProcessor(
			    tokenizer=a_		,      image_processor=a_		,      qformer_tokenizer=a_	)

			lowerCAmelCase     :    Tuple						=   "lower newer"

			lowerCAmelCase     :    Optional[Any]						=   processor(text=a_	)

			lowerCAmelCase     :    int						=   tokenizer(a_		,      return_token_type_ids=a_	)
			lowerCAmelCase     :    Dict						=   qformer_tokenizer(a_		,      return_token_type_ids=a_	)

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

			for key in encoded_tokens_qformer.keys():
					self.assertListEqual(encoded_tokens_qformer[key]		,      encoded_processor["qformer_" + key]	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_image_processor()
			lowerCAmelCase     :    List[Any]						=   self.get_tokenizer()
			lowerCAmelCase     :    Union[str, Any]						=   self.get_qformer_tokenizer()

			lowerCAmelCase     :    Dict						=   InstructBlipProcessor(
			    tokenizer=a_		,      image_processor=a_		,      qformer_tokenizer=a_	)

			lowerCAmelCase     :    int						=   "lower newer"
			lowerCAmelCase     :    List[str]						=   self.prepare_image_inputs()

			lowerCAmelCase     :    int						=   processor(text=a_		,      images=a_	)

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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.get_image_processor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()
			lowerCAmelCase     :    str						=   self.get_qformer_tokenizer()

			lowerCAmelCase     :    Tuple						=   InstructBlipProcessor(
			    tokenizer=a_		,      image_processor=a_		,      qformer_tokenizer=a_	)

			lowerCAmelCase     :    List[Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCAmelCase     :    Dict						=   processor.batch_decode(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.batch_decode(a_	)

			self.assertListEqual(a_		,      a_	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.get_image_processor()
			lowerCAmelCase     :    Dict						=   self.get_tokenizer()
			lowerCAmelCase     :    Union[str, Any]						=   self.get_qformer_tokenizer()

			lowerCAmelCase     :    Tuple						=   InstructBlipProcessor(
			    tokenizer=a_		,      image_processor=a_		,      qformer_tokenizer=a_	)

			lowerCAmelCase     :    Optional[Any]						=   "lower newer"
			lowerCAmelCase     :    int						=   self.prepare_image_inputs()

			lowerCAmelCase     :    Optional[int]						=   processor(text=a_		,      images=a_	)

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










'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 		lowerCamelCase  (      _A      ,   _A      ,   _A       ):



	@register_to_config
	def __init__(   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_ = False		,      ):
			super().__init__()

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)
			lowerCAmelCase     :    int						=   False

			lowerCAmelCase     :    int						=   nn.Dropout(p=a_	)

			lowerCAmelCase     :    str						=   TaConfig(
			    vocab_size=a_		,      d_model=a_		,      num_heads=a_		,      d_kv=a_		,      d_ff=a_		,      dropout_rate=a_		,      feed_forward_proj=a_		,      is_decoder=a_		,      is_encoder_decoder=a_		,      )

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for lyr_num in range(a_	):
					lowerCAmelCase     :    Optional[Any]						=   TaBlock(a_	)
					self.encoders.append(a_	)

			lowerCAmelCase     :    List[str]						=   TaLayerNorm(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   nn.Dropout(p=a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.token_embedder(a_	)

			lowerCAmelCase     :    int						=   encoder_input_tokens.shape[1]
			lowerCAmelCase     :    Optional[int]						=   torch.arange(a_		,      device=encoder_input_tokens.device	)
			x += self.position_encoding(a_	)

			lowerCAmelCase     :    List[str]						=   self.dropout_pre(a_	)

			# inverted the attention mask
			lowerCAmelCase     :    List[Any]						=   encoder_input_tokens.size()
			lowerCAmelCase     :    Dict						=   self.get_extended_attention_mask(a_		,      a_	)

			for lyr in self.encoders:
					lowerCAmelCase     :    Dict						=   lyr(a_		,      a_	)[0]
			lowerCAmelCase     :    int						=   self.layer_norm(a_	)

			return self.dropout_post(a_	), encoder_inputs_mask



