




"""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						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    tempfile.mkdtemp()

							_lowerCamelCase     :			Union[str, Any]    =    BlipImageProcessor()
							_lowerCamelCase     :			Optional[int]    =    GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'  )
							_lowerCamelCase     :			str    =    BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'  )

							_lowerCamelCase     :			Optional[Any]    =    InstructBlipProcessor(lowercase  ,      lowercase  ,      lowercase  )

							processor.save_pretrained(self.tmpdirname  )
			def 					A_    (  self  ,      **lowercase  ):
							return AutoProcessor.from_pretrained(self.tmpdirname  ,      **lowercase  ).tokenizer
			def 					A_    (  self  ,      **lowercase  ):
							return AutoProcessor.from_pretrained(self.tmpdirname  ,      **lowercase  ).image_processor
			def 					A_    (  self  ,      **lowercase  ):
							return AutoProcessor.from_pretrained(self.tmpdirname  ,      **lowercase  ).qformer_tokenizer
			def 					A_    (  self  ):
							shutil.rmtree(self.tmpdirname  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    [np.random.randint(255  ,      size=(3, 30, 400)  ,      dtype=np.uinta  )]

							_lowerCamelCase     :			int    =    [Image.fromarray(np.moveaxis(lowercase  ,      0  ,      -1  )  ) for x in image_inputs]

							return image_inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    InstructBlipProcessor(
							    tokenizer=self.get_tokenizer()  ,      image_processor=self.get_image_processor()  ,      qformer_tokenizer=self.get_qformer_tokenizer()  ,      )
							processor.save_pretrained(self.tmpdirname  )

							_lowerCamelCase     :			Optional[int]    =    self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'  )
							_lowerCamelCase     :			str    =    self.get_image_processor(do_normalize=lowercase  ,      padding_value=1.0  )

							_lowerCamelCase     :			Union[str, Any]    =    InstructBlipProcessor.from_pretrained(
							    self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=lowercase  ,      padding_value=1.0  )

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

							self.assertEqual(processor.image_processor.to_json_string()  ,      image_processor_add_kwargs.to_json_string()  )
							self.assertIsInstance(processor.image_processor  ,      lowercase  )
							self.assertIsInstance(processor.qformer_tokenizer  ,      lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    self.get_image_processor()
							_lowerCamelCase     :			Union[str, Any]    =    self.get_tokenizer()
							_lowerCamelCase     :			List[Any]    =    self.get_qformer_tokenizer()

							_lowerCamelCase     :			List[Any]    =    InstructBlipProcessor(
							    tokenizer=lowercase  ,      image_processor=lowercase  ,      qformer_tokenizer=lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    self.prepare_image_inputs()

							_lowerCamelCase     :			Optional[Any]    =    image_processor(lowercase  ,      return_tensors='np'  )
							_lowerCamelCase     :			Any    =    processor(images=lowercase  ,      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 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    self.get_image_processor()
							_lowerCamelCase     :			Tuple    =    self.get_tokenizer()
							_lowerCamelCase     :			Tuple    =    self.get_qformer_tokenizer()

							_lowerCamelCase     :			Optional[int]    =    InstructBlipProcessor(
							    tokenizer=lowercase  ,      image_processor=lowercase  ,      qformer_tokenizer=lowercase  )

							_lowerCamelCase     :			Dict    =    'lower newer'

							_lowerCamelCase     :			Dict    =    processor(text=lowercase  )

							_lowerCamelCase     :			Optional[Any]    =    tokenizer(lowercase  ,      return_token_type_ids=lowercase  )
							_lowerCamelCase     :			Any    =    qformer_tokenizer(lowercase  ,      return_token_type_ids=lowercase  )

							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 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.get_image_processor()
							_lowerCamelCase     :			Optional[int]    =    self.get_tokenizer()
							_lowerCamelCase     :			Tuple    =    self.get_qformer_tokenizer()

							_lowerCamelCase     :			Union[str, Any]    =    InstructBlipProcessor(
							    tokenizer=lowercase  ,      image_processor=lowercase  ,      qformer_tokenizer=lowercase  )

							_lowerCamelCase     :			Tuple    =    'lower newer'
							_lowerCamelCase     :			Union[str, Any]    =    self.prepare_image_inputs()

							_lowerCamelCase     :			int    =    processor(text=lowercase  ,      images=lowercase  )

							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(lowercase  ):
											processor()
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    self.get_image_processor()
							_lowerCamelCase     :			int    =    self.get_tokenizer()
							_lowerCamelCase     :			Optional[Any]    =    self.get_qformer_tokenizer()

							_lowerCamelCase     :			Optional[int]    =    InstructBlipProcessor(
							    tokenizer=lowercase  ,      image_processor=lowercase  ,      qformer_tokenizer=lowercase  )

							_lowerCamelCase     :			List[str]    =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							_lowerCamelCase     :			List[Any]    =    processor.batch_decode(lowercase  )
							_lowerCamelCase     :			str    =    tokenizer.batch_decode(lowercase  )

							self.assertListEqual(lowercase  ,      lowercase  )






			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    self.get_image_processor()
							_lowerCamelCase     :			int    =    self.get_tokenizer()
							_lowerCamelCase     :			str    =    self.get_qformer_tokenizer()

							_lowerCamelCase     :			Any    =    InstructBlipProcessor(
							    tokenizer=lowercase  ,      image_processor=lowercase  ,      qformer_tokenizer=lowercase  )

							_lowerCamelCase     :			int    =    'lower newer'
							_lowerCamelCase     :			str    =    self.prepare_image_inputs()

							_lowerCamelCase     :			Union[str, Any]    =    processor(text=lowercase  ,      images=lowercase  )

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





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )