
'''simple docstring'''





import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import FlavaImageProcessor, FlavaProcessor
						from transformers.models.flava.image_processing_flava import (
						    FLAVA_CODEBOOK_MEAN,
						    FLAVA_CODEBOOK_STD,
						    FLAVA_IMAGE_MEAN,
						    FLAVA_IMAGE_STD,
						)

@require_vision
class     a (					unittest.TestCase     ):




				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  List[Any]			=     tempfile.mkdtemp()

										# fmt: off
										snake_case							:  Dict			=     ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
										# fmt: on
										snake_case							:  List[str]			=     os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['''vocab_file''']   )

										with open(self.vocab_file,     '''w''',     encoding='''utf-8'''   ) as fp:
																fp.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )

										snake_case							:  int			=     {
										    '''image_mean''': FLAVA_IMAGE_MEAN,
										    '''image_std''': FLAVA_IMAGE_STD,
										    '''do_normalize''': True,
										    '''do_resize''': True,
										    '''size''': 2_24,
										    '''do_center_crop''': True,
										    '''crop_size''': 2_24,
										    '''input_size_patches''': 14,
										    '''total_mask_patches''': 75,
										    '''mask_group_max_patches''': None,
										    '''mask_group_min_patches''': 16,
										    '''mask_group_min_aspect_ratio''': 0.3,
										    '''mask_group_max_aspect_ratio''': None,
										    '''codebook_do_resize''': True,
										    '''codebook_size''': 1_12,
										    '''codebook_do_center_crop''': True,
										    '''codebook_crop_size''': 1_12,
										    '''codebook_do_map_pixels''': True,
										    '''codebook_do_normalize''': True,
										    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
										    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
										}

										snake_case							:  int			=     os.path.join(self.tmpdirname,     SCREAMING_SNAKE_CASE_   )
										with open(self.image_processor_file,     '''w''',     encoding='''utf-8'''   ) as fp:
																json.dump(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Optional[int],     **SCREAMING_SNAKE_CASE_						:					int   ):
										return BertTokenizer.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					str,     **SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return BertTokenizerFast.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										return FlavaImageProcessor.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										shutil.rmtree(self.tmpdirname   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Tuple			=     [np.random.randint(2_55,     size=(3, 30, 4_00),     dtype=np.uinta   )]

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

										return image_inputs




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Optional[int]			=     self.get_tokenizer()
										snake_case							:  str			=     self.get_rust_tokenizer()
										snake_case							:  Any			=     self.get_image_processor()

										snake_case							:  int			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )
										processor_slow.save_pretrained(self.tmpdirname   )
										snake_case							:  Any			=     FlavaProcessor.from_pretrained(self.tmpdirname,     use_fast=SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )
										processor_fast.save_pretrained(self.tmpdirname   )
										snake_case							:  Optional[int]			=     FlavaProcessor.from_pretrained(self.tmpdirname   )

										self.assertEqual(processor_slow.tokenizer.get_vocab(),     tokenizer_slow.get_vocab()   )
										self.assertEqual(processor_fast.tokenizer.get_vocab(),     tokenizer_fast.get_vocab()   )
										self.assertEqual(tokenizer_slow.get_vocab(),     tokenizer_fast.get_vocab()   )
										self.assertIsInstance(processor_slow.tokenizer,     SCREAMING_SNAKE_CASE_   )
										self.assertIsInstance(processor_fast.tokenizer,     SCREAMING_SNAKE_CASE_   )

										self.assertEqual(processor_slow.image_processor.to_json_string(),     image_processor.to_json_string()   )
										self.assertEqual(processor_fast.image_processor.to_json_string(),     image_processor.to_json_string()   )
										self.assertIsInstance(processor_slow.image_processor,     SCREAMING_SNAKE_CASE_   )
										self.assertIsInstance(processor_fast.image_processor,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  int			=     FlavaProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()   )
										processor.save_pretrained(self.tmpdirname   )

										snake_case							:  Union[str, Any]			=     self.get_tokenizer(bos_token='''(BOS)''',     eos_token='''(EOS)'''   )
										snake_case							:  Optional[Any]			=     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,     padding_value=1.0   )

										snake_case							:  Tuple			=     FlavaProcessor.from_pretrained(
										    self.tmpdirname,     bos_token='''(BOS)''',     eos_token='''(EOS)''',     do_normalize=SCREAMING_SNAKE_CASE_,     padding_value=1.0   )

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

										self.assertEqual(processor.image_processor.to_json_string(),     image_processor_add_kwargs.to_json_string()   )
										self.assertIsInstance(processor.image_processor,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  List[str]			=     self.get_image_processor()
										snake_case							:  List[str]			=     self.get_tokenizer()

										snake_case							:  int			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  Optional[int]			=     image_processor(SCREAMING_SNAKE_CASE_,     return_tensors='''np'''   )
										snake_case							:  Optional[int]			=     processor(images=SCREAMING_SNAKE_CASE_,     return_tensors='''np'''   )

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),     input_processor[key].sum(),     delta=1e-2   )

										# With rest of the args
										random.seed(12_34   )
										snake_case							:  Dict			=     image_processor(
										    SCREAMING_SNAKE_CASE_,     return_image_mask=SCREAMING_SNAKE_CASE_,     return_codebook_pixels=SCREAMING_SNAKE_CASE_,     return_tensors='''np'''   )
										random.seed(12_34   )
										snake_case							:  Union[str, Any]			=     processor(
										    images=SCREAMING_SNAKE_CASE_,     return_image_mask=SCREAMING_SNAKE_CASE_,     return_codebook_pixels=SCREAMING_SNAKE_CASE_,     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 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  List[Any]			=     self.get_image_processor()
										snake_case							:  Dict			=     self.get_tokenizer()

										snake_case							:  Optional[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Any			=     '''lower newer'''

										snake_case							:  Tuple			=     processor(text=SCREAMING_SNAKE_CASE_   )

										snake_case							:  int			=     tokenizer(SCREAMING_SNAKE_CASE_   )

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




				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Any			=     self.get_image_processor()
										snake_case							:  Optional[Any]			=     self.get_tokenizer()

										snake_case							:  Optional[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  int			=     '''lower newer'''
										snake_case							:  Optional[int]			=     self.prepare_image_inputs()

										snake_case							:  List[str]			=     processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_   )

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

										# add extra args
										snake_case							:  Tuple			=     processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_,     return_codebook_pixels=SCREAMING_SNAKE_CASE_,     return_image_mask=SCREAMING_SNAKE_CASE_   )

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

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




				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Any			=     self.get_image_processor()
										snake_case							:  Union[str, Any]			=     self.get_tokenizer()

										snake_case							:  Dict			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

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

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

										self.assertListEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  int			=     self.get_image_processor()
										snake_case							:  Union[str, Any]			=     self.get_tokenizer()

										snake_case							:  List[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     '''lower newer'''
										snake_case							:  Any			=     self.prepare_image_inputs()

										snake_case							:  Optional[Any]			=     processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_   )

										self.assertListEqual(list(inputs.keys()   ),     processor.model_input_names   )



'''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


UpperCAmelCase            = logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						UpperCAmelCase            = object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf

class     a :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					Optional[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return self._value




				def 						__snake_case				(				self						:					List[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return str(self._value   )




class     a (					__magic_name__					,__magic_name__     ):




				def 						__snake_case				(				self						:					Optional[Any]   ):
										return self._value




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  Union[str, Any]			=     None
										snake_case							:  List[Any]			=     None
										snake_case							:  List[str]			=     None

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




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

										if self._path is not None:
																snake_case							:  str			=     Image.open(self._path   )
																return self._raw




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

										if self._raw is not None:
																snake_case							:  int			=     tempfile.mkdtemp()
																snake_case							:  str			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )
																self._raw.save(self._path   )

																return self._path

										if self._tensor is not None:
																snake_case							:  Tuple			=     self._tensor.cpu().detach().numpy()

																# There is likely simpler than load into image into save
																snake_case							:  str			=     Image.fromarray((array * 2_55).astype(np.uinta   )   )

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

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

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




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

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




				def 						__snake_case				(				self						:					List[str]   ):
										if self._tensor is not None:
																return self._tensor

										if self._path is not None:
																snake_case,       snake_case							:  Optional[Any]			=     sf.read(self._path   )
																snake_case							:  Any			=     torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.wav'''   )
																sf.write(self._path,     self._tensor,     samplerate=self.samplerate   )
																return self._path




UpperCAmelCase            = {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCAmelCase            = {str: AgentText}

if is_vision_available():
						UpperCAmelCase            = AgentImage

def   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, Any]			=     {}
												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:
																														snake_case							:  Optional[int]			=     AGENT_TYPE_MAPPING[output_types[i]](A_							)
																								else:
																														snake_case							:  List[str]			=     AgentType(A_							)

																		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(A_     ,				_k							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

						elif isinstance(A_     ,				(list, tuple)							):
												snake_case							:  Union[str, Any]			=     type(A_							)()
												for i, v in enumerate(A_							):
																		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]](A_							)							)
																								else:
																														decoded_outputs.append(AgentType(A_							)							)
																		else:
																								# If the class does not have defined output, then we map according to the type
																								snake_case							:  Union[str, Any]			=     False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				decoded_outputs.append(_v(A_							)							)
																																				snake_case							:  Tuple			=     True

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

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

												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(A_     ,				_k							):
																														return _v(A_							)
																		return AgentType(A_							)

						return decoded_outputs

