

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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       tempfile.mkdtemp()

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       InstructBlipProcessor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									processor.save_pretrained(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						**lowerCAmelCase__  :     List[str]):
									return AutoProcessor.from_pretrained(self.tmpdirname   ,						**lowerCAmelCase__).tokenizer



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						**lowerCAmelCase__  :     Tuple):
									return AutoProcessor.from_pretrained(self.tmpdirname   ,						**lowerCAmelCase__).image_processor



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						**lowerCAmelCase__  :     Union[str, Any]):
									return AutoProcessor.from_pretrained(self.tmpdirname   ,						**lowerCAmelCase__).qformer_tokenizer



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									shutil.rmtree(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Dict  		=       [np.random.randint(255   ,						size=(3, 30, 400)   ,						dtype=np.uinta)]

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

									return image_inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()   ,						image_processor=self.get_image_processor()   ,						qformer_tokenizer=self.get_qformer_tokenizer()   ,						)
									processor.save_pretrained(self.tmpdirname)

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer(bos_token="(BOS)"   ,						eos_token="(EOS)")
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_image_processor(do_normalize=lowerCAmelCase__   ,						padding_value=1.0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       InstructBlipProcessor.from_pretrained(
									    self.tmpdirname   ,						bos_token="(BOS)"   ,						eos_token="(EOS)"   ,						do_normalize=lowerCAmelCase__   ,						padding_value=1.0)

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

									self.assertEqual(processor.image_processor.to_json_string()   ,						image_processor_add_kwargs.to_json_string())
									self.assertIsInstance(processor.image_processor   ,						lowerCAmelCase__)
									self.assertIsInstance(processor.qformer_tokenizer   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_image_processor()
									SCREAMING_SNAKE_CASE_: str  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.get_qformer_tokenizer()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       InstructBlipProcessor(
									    tokenizer=lowerCAmelCase__   ,						image_processor=lowerCAmelCase__   ,						qformer_tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_: Tuple  		=       image_processor(lowerCAmelCase__   ,						return_tensors="np")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       processor(images=lowerCAmelCase__   ,						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        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_image_processor()
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.get_qformer_tokenizer()

									SCREAMING_SNAKE_CASE_: int  		=       InstructBlipProcessor(
									    tokenizer=lowerCAmelCase__   ,						image_processor=lowerCAmelCase__   ,						qformer_tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       "lower newer"

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       processor(text=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       tokenizer(lowerCAmelCase__   ,						return_token_type_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       qformer_tokenizer(lowerCAmelCase__   ,						return_token_type_ids=lowerCAmelCase__)

									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        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.get_image_processor()
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_qformer_tokenizer()

									SCREAMING_SNAKE_CASE_: Tuple  		=       InstructBlipProcessor(
									    tokenizer=lowerCAmelCase__   ,						image_processor=lowerCAmelCase__   ,						qformer_tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       "lower newer"
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_: Dict  		=       processor(text=lowerCAmelCase__   ,						images=lowerCAmelCase__)

									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(lowerCAmelCase__):
															processor()



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_image_processor()
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_qformer_tokenizer()

									SCREAMING_SNAKE_CASE_: Any  		=       InstructBlipProcessor(
									    tokenizer=lowerCAmelCase__   ,						image_processor=lowerCAmelCase__   ,						qformer_tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE_: str  		=       processor.batch_decode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.batch_decode(lowerCAmelCase__)

									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_image_processor()
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_qformer_tokenizer()

									SCREAMING_SNAKE_CASE_: Tuple  		=       InstructBlipProcessor(
									    tokenizer=lowerCAmelCase__   ,						image_processor=lowerCAmelCase__   ,						qformer_tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "lower newer"
									SCREAMING_SNAKE_CASE_: Dict  		=       self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_: int  		=       processor(text=lowerCAmelCase__   ,						images=lowerCAmelCase__)

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






