




"""simple docstring"""

import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_lowerCamelCase					:     str				       =    logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		_lowerCamelCase					:     List[Any]				       =    object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf

class     lowercase :







				def __init__(							self				:   Optional[Any] ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						value







				def __str__(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					return self.to_string()







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					logger.error(
					    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''    )
					return self._value







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					logger.error(
					    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''    )
					return str(self._value    )

class     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					return self._value







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					return self._value

class     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):







				def __init__(							self				:   Tuple ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					super().__init__(_lowerCamelCase    )

					if not is_vision_available():
						raise ImportError('''PIL must be installed in order to handle images.'''    )

					A_					:			Union[str, Any]						=						None
					A_					:			Tuple						=						None
					A_					:			int						=						None

					if isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						A_					:			Tuple						=						value
					elif isinstance(_lowerCamelCase ,       (str, pathlib.Path)    ):
						A_					:			Union[str, Any]						=						value
					elif isinstance(_lowerCamelCase ,       torch.Tensor    ):
						A_					:			Dict						=						value
					else:
						raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(_lowerCamelCase    )}"""    )







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Optional[int]=None    ):



					"""simple docstring"""

					from IPython.display import Image, display

					display(Image(self.to_string()    )    )







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					if self._raw is not None:
						return self._raw

					if self._path is not None:
						A_					:			int						=						Image.open(self._path    )
						return self._raw







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					if self._path is not None:
						return self._path

					if self._raw is not None:
						A_					:			Dict						=						tempfile.mkdtemp()
						A_					:			Tuple						=						os.path.join(_lowerCamelCase ,       str(uuid.uuida()    ) + '''.png'''    )
						self._raw.save(self._path    )

						return self._path

					if self._tensor is not None:
						A_					:			Any						=						self._tensor.cpu().detach().numpy()

						# There is likely simpler than load into image into save
						A_					:			Union[str, Any]						=						Image.fromarray((array * 2_55).astype(np.uinta    )    )

						A_					:			Optional[Any]						=						tempfile.mkdtemp()
						A_					:			List[Any]						=						os.path.join(_lowerCamelCase ,       str(uuid.uuida()    ) + '''.png'''    )

						img.save(self._path    )

						return self._path

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Dict ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   str=1_60_00    ):



					"""simple docstring"""

					super().__init__(_lowerCamelCase    )

					if not is_soundfile_availble():
						raise ImportError('''soundfile must be installed in order to handle audio.'''    )

					A_					:			Optional[Any]						=						None
					A_					:			str						=						None

					A_					:			Any						=						samplerate

					if isinstance(_lowerCamelCase ,       (str, pathlib.Path)    ):
						A_					:			Optional[int]						=						value
					elif isinstance(_lowerCamelCase ,       torch.Tensor    ):
						A_					:			Optional[Any]						=						value
					else:
						raise ValueError(F"""Unsupported audio type: {type(_lowerCamelCase    )}"""    )







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Optional[Any]=None ,       _lowerCamelCase				:   Union[str, Any]=None    ):



					"""simple docstring"""

					from IPython.display import Audio, display

					display(Audio(self.to_string() ,       rate=self.samplerate    )    )







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					if self._tensor is not None:
						return self._tensor

					if self._path is not None:
						A_					:			Dict						=						sf.read(self._path    )
						A_					:			Union[str, Any]						=						torch.tensor(_lowerCamelCase    )
						return self._tensor







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					if self._path is not None:
						return self._path

					if self._tensor is not None:
						A_					:			Any						=						tempfile.mkdtemp()
						A_					:			Tuple						=						os.path.join(_lowerCamelCase ,       str(uuid.uuida()    ) + '''.wav'''    )
						sf.write(self._path ,       self._tensor ,       samplerate=self.samplerate    )
						return self._path
_lowerCamelCase					:     Union[str, Any]				       =    {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_lowerCamelCase					:     str				       =    {str: AgentText}

if is_vision_available():
		_lowerCamelCase					:     List[str]				       =    AgentImage

def 						lowercase_ (						*_UpperCAmelCase					, **_UpperCAmelCase  ):




	"""simple docstring"""






	A_					:			Any						=						[(arg.to_raw() if isinstance(_UpperCAmelCase					, _UpperCAmelCase  ) else arg) for arg in args]
	A_					:			Optional[Any]						=						{k: (v.to_raw() if isinstance(_UpperCAmelCase					, _UpperCAmelCase  ) else v) for k, v in kwargs.items()}
	return args, kwargs

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=None  ):




	"""simple docstring"""






	if isinstance(_UpperCAmelCase					, _UpperCAmelCase  ):
		A_					:			Tuple						=						{}
		for i, (k, v) in enumerate(outputs.items()  ):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					A_					:			str						=						AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase  )
				else:
					A_					:			Optional[int]						=						AgentType(_UpperCAmelCase  )

			else:
				# If the class does not have defined output, then we map according to the type
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(_UpperCAmelCase					, _k  ):
						A_					:			List[str]						=						_v(_UpperCAmelCase  )
				if k not in decoded_outputs:
					A_					:			Union[str, Any]						=						AgentType[v]

	elif isinstance(_UpperCAmelCase					, (list, tuple)  ):
		A_					:			Optional[int]						=						type(_UpperCAmelCase  )()
		for i, v in enumerate(_UpperCAmelCase  ):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase  )  )
				else:
					decoded_outputs.append(AgentType(_UpperCAmelCase  )  )
			else:
				# If the class does not have defined output, then we map according to the type
				A_					:			List[Any]						=						False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(_UpperCAmelCase					, _k  ):
						decoded_outputs.append(_v(_UpperCAmelCase  )  )
						A_					:			str						=						True

				if not found:
					decoded_outputs.append(AgentType(_UpperCAmelCase  )  )

	else:
		if output_types[0] in AGENT_TYPE_MAPPING:
			# If the class has defined outputs, we can map directly according to the class definition
			A_					:			Any						=						AGENT_TYPE_MAPPING[output_types[0]](_UpperCAmelCase  )

		else:
			# If the class does not have defined output, then we map according to the type
			for _k, _v in INSTANCE_TYPE_MAPPING.items():
				if isinstance(_UpperCAmelCase					, _k  ):
					return _v(_UpperCAmelCase  )
			return AgentType(_UpperCAmelCase  )

	return decoded_outputs











"""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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

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







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

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







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

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

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.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    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

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

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





