import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
							from PIL import Image

							from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()

												# fmt: off
												SCREAMING_SNAKE_CASE    			=       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
												# fmt: on
												SCREAMING_SNAKE_CASE    			=       os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["vocab_file"]      )
												with open(self.vocab_file	,							"w"	,							encoding="utf-8"      ) as vocab_writer:
																	vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )

												SCREAMING_SNAKE_CASE    			=       {
												    "do_resize": True,
												    "size": {"height": 18, "width": 18},
												    "do_normalize": True,
												    "image_mean": [0.5, 0.5, 0.5],
												    "image_std": [0.5, 0.5, 0.5],
												}
												SCREAMING_SNAKE_CASE    			=       os.path.join(self.tmpdirname	,							_UpperCamelCase      )
												with open(self.image_processor_file	,							"w"	,							encoding="utf-8"      ) as fp:
																	json.dump(_UpperCamelCase	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Tuple	,							**_UpperCamelCase   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												return BertTokenizer.from_pretrained(self.tmpdirname	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												return ViTImageProcessor.from_pretrained(self.tmpdirname	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname      )




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [np.random.randint(255	,							size=(3, 30, 400)	,							dtype=np.uinta      )]

												SCREAMING_SNAKE_CASE    			=       [Image.fromarray(np.moveaxis(_UpperCamelCase	,							0	,							-1      )      ) for x in image_inputs]

												return image_inputs




							def 	__snake_case(						self   :					str      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												processor.save_pretrained(self.tmpdirname      )
												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

												self.assertEqual(processor.tokenizer.get_vocab()	,							tokenizer.get_vocab()      )
												self.assertIsInstance(processor.tokenizer	,							(BertTokenizer, BertTokenizerFast)      )

												self.assertEqual(processor.image_processor.to_json_string()	,							image_processor.to_json_string()      )
												self.assertIsInstance(processor.image_processor	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(
												    tokenizer=self.get_tokenizer()	,							image_processor=self.get_image_processor()      )
												processor.save_pretrained(self.tmpdirname      )

												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer(bos_token="(BOS)"	,							eos_token="(EOS)"      )
												SCREAMING_SNAKE_CASE    			=       self.get_image_processor(do_normalize=_UpperCamelCase	,							padding_value=1.0      )

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor.from_pretrained(
												    self.tmpdirname	,							bos_token="(BOS)"	,							eos_token="(EOS)"	,							do_normalize=_UpperCamelCase	,							padding_value=1.0      )

												self.assertEqual(processor.tokenizer.get_vocab()	,							tokenizer_add_kwargs.get_vocab()      )
												self.assertIsInstance(processor.tokenizer	,							(BertTokenizer, BertTokenizerFast)      )

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




							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE    			=       image_processor(_UpperCamelCase	,							return_tensors="np"      )
												SCREAMING_SNAKE_CASE    			=       processor(images=_UpperCamelCase	,							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   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "lower newer"

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       tokenizer(_UpperCamelCase      )

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




							def 	__snake_case(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "lower newer"
												SCREAMING_SNAKE_CASE    			=       self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase	,							images=_UpperCamelCase      )

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

												# test if it raises when no input is passed
												with self.assertRaises(_UpperCamelCase      ):
																	processor()




							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												SCREAMING_SNAKE_CASE    			=       processor.batch_decode(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.batch_decode(_UpperCamelCase      )

												self.assertListEqual(_UpperCamelCase	,							_UpperCamelCase      )





							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()

												SCREAMING_SNAKE_CASE    			=       VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "lower newer"
												SCREAMING_SNAKE_CASE    			=       self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase	,							images=_UpperCamelCase      )

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





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy




