



import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


A__       =			logging.get_logger(__name__)





@dataclass
class    __lowerCAmelCase					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							42
				__lowerCamelCase					=							None
				__lowerCamelCase					=							None




				def 				snake_case  (				self						):



									"""simple docstring"""





									return json.dumps(dataclasses.asdict(self						)		,							indent=2						) + "\n"





@dataclass(frozen=lowerCamelCase__			)
class    __lowerCAmelCase					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							None
				__lowerCamelCase					=							None
				__lowerCamelCase					=							None




				def 				snake_case  (				self						):



									"""simple docstring"""





									return json.dumps(dataclasses.asdict(self						)						) + "\n"





class    __lowerCAmelCase					:




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									raise NotImplementedError()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									raise NotImplementedError()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									raise NotImplementedError()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									raise NotImplementedError()




				def 				snake_case  (				self						):



									"""simple docstring"""





									raise NotImplementedError()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if len(self.get_labels()						) > 1:
														_lowerCAmelCase         =       self.get_labels()[int(example.label						)]
									return example




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							_snake_case=None						):



									"""simple docstring"""





									with open(_UpperCAmelCase		,							"""r"""		,							encoding="""utf-8-sig"""						) as f:
														return list(csv.reader(_UpperCAmelCase		,							delimiter="""\t"""		,							quotechar=_UpperCAmelCase						)						)





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case=None		,							_snake_case=None		,							_snake_case="classification"		,							_snake_case=False						):



									"""simple docstring"""





									_lowerCAmelCase         =       [] if labels is None else labels
									_lowerCAmelCase         =       [] if examples is None else examples
									_lowerCAmelCase         =       mode
									_lowerCAmelCase         =       verbose




				def __len__(				self						):



									"""simple docstring"""





									return len(self.examples						)




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									if isinstance(_UpperCAmelCase		,							_UpperCAmelCase						):
														return SingleSentenceClassificationProcessor(labels=self.labels		,							examples=self.examples[idx]						)
									return self.examples[idx]




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							_snake_case=""		,							_snake_case=0		,							_snake_case=1		,							_snake_case=None		,							_snake_case=False		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       cls(**_UpperCAmelCase						)
									processor.add_examples_from_csv(
									    _UpperCAmelCase		,							split_name=_UpperCAmelCase		,							column_label=_UpperCAmelCase		,							column_text=_UpperCAmelCase		,							column_id=_UpperCAmelCase		,							skip_first_row=_UpperCAmelCase		,							overwrite_labels=_UpperCAmelCase		,							overwrite_examples=_UpperCAmelCase		,							)
									return processor




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							_snake_case=None		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       cls(**_UpperCAmelCase						)
									processor.add_examples(_UpperCAmelCase		,							labels=_UpperCAmelCase						)
									return processor




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=""		,							_snake_case=0		,							_snake_case=1		,							_snake_case=None		,							_snake_case=False		,							_snake_case=False		,							_snake_case=False		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       self._read_tsv(_UpperCAmelCase						)
									if skip_first_row:
														_lowerCAmelCase         =       lines[1:]
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									for i, line in enumerate(_UpperCAmelCase						):
														texts.append(line[column_text]						)
														labels.append(line[column_label]						)
														if column_id is not None:
																			ids.append(line[column_id]						)
														else:
																			_lowerCAmelCase         =       F'{split_name}-{i}' if split_name else str(_UpperCAmelCase						)
																			ids.append(_UpperCAmelCase						)

									return self.add_examples(
									    _UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							overwrite_labels=_UpperCAmelCase		,							overwrite_examples=_UpperCAmelCase						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=None		,							_snake_case=None		,							_snake_case=False		,							_snake_case=False						):



									"""simple docstring"""





									if labels is not None and len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
														raise ValueError(
														    F'Text and labels have mismatched lengths {len(_UpperCAmelCase						)} and {len(_UpperCAmelCase						)}'						)
									if ids is not None and len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
														raise ValueError(F'Text and ids have mismatched lengths {len(_UpperCAmelCase						)} and {len(_UpperCAmelCase						)}'						)
									if ids is None:
														_lowerCAmelCase         =       [None] * len(_UpperCAmelCase						)
									if labels is None:
														_lowerCAmelCase         =       [None] * len(_UpperCAmelCase						)
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       set()
									for text_or_text_and_label, label, guid in zip(_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase						):
														if isinstance(_UpperCAmelCase		,							(tuple, list)						) and label is None:
																			_lowerCAmelCase      ,      _lowerCAmelCase         =       text_or_text_and_label
														else:
																			_lowerCAmelCase         =       text_or_text_and_label
														added_labels.add(_UpperCAmelCase						)
														examples.append(InputExample(guid=_UpperCAmelCase		,							text_a=_UpperCAmelCase		,							text_b=_UpperCAmelCase		,							label=_UpperCAmelCase						)						)

									# Update examples
									if overwrite_examples:
														_lowerCAmelCase         =       examples
									else:
														self.examples.extend(_UpperCAmelCase						)

									# Update labels
									if overwrite_labels:
														_lowerCAmelCase         =       list(_UpperCAmelCase						)
									else:
														_lowerCAmelCase         =       list(set(self.labels						).union(_UpperCAmelCase						)						)

									return self.examples




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=None		,							_snake_case=False		,							_snake_case=0		,							_snake_case=True		,							_snake_case=None		,							):



									"""simple docstring"""





									if max_length is None:
														_lowerCAmelCase         =       tokenizer.max_len

									_lowerCAmelCase         =       {label: i for i, label in enumerate(self.labels						)}

									_lowerCAmelCase         =       []
									for ex_index, example in enumerate(self.examples						):
														if ex_index % 10000 == 0:
																			logger.info(F'Tokenizing example {ex_index}'						)

														_lowerCAmelCase         =       tokenizer.encode(
														    example.text_a		,							add_special_tokens=_UpperCAmelCase		,							max_length=min(_UpperCAmelCase		,							tokenizer.max_len						)		,							)
														all_input_ids.append(_UpperCAmelCase						)

									_lowerCAmelCase         =       max(len(_UpperCAmelCase						) for input_ids in all_input_ids						)

									_lowerCAmelCase         =       []
									for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase		,							self.examples						)						):
														if ex_index % 10000 == 0:
																			logger.info(F'Writing example {ex_index}/{len(self.examples						)}'						)
														# The mask has 1 for real tokens and 0 for padding tokens. Only real
														# tokens are attended to.
														_lowerCAmelCase         =       [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase						)

														# Zero-pad up to the sequence length.
														_lowerCAmelCase         =       batch_length - len(_UpperCAmelCase						)
														if pad_on_left:
																			_lowerCAmelCase         =       ([pad_token] * padding_length) + input_ids
																			_lowerCAmelCase         =       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																			_lowerCAmelCase         =       input_ids + ([pad_token] * padding_length)
																			_lowerCAmelCase         =       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

														if len(_UpperCAmelCase						) != batch_length:
																			raise ValueError(F'Error with input length {len(_UpperCAmelCase						)} vs {batch_length}'						)
														if len(_UpperCAmelCase						) != batch_length:
																			raise ValueError(F'Error with input length {len(_UpperCAmelCase						)} vs {batch_length}'						)

														if self.mode == "classification":
																			_lowerCAmelCase         =       label_map[example.label]
														elif self.mode == "regression":
																			_lowerCAmelCase         =       float(example.label						)
														else:
																			raise ValueError(self.mode						)

														if ex_index < 5 and self.verbose:
																			logger.info("""*** Example ***"""						)
																			logger.info(F'guid: {example.guid}'						)
																			logger.info(F'input_ids: {" ".join([str(_UpperCAmelCase						) for x in input_ids]						)}'						)
																			logger.info(F'attention_mask: {" ".join([str(_UpperCAmelCase						) for x in attention_mask]						)}'						)
																			logger.info(F'label: {example.label} (id = {label})'						)

														features.append(InputFeatures(input_ids=_UpperCAmelCase		,							attention_mask=_UpperCAmelCase		,							label=_UpperCAmelCase						)						)

									if return_tensors is None:
														return features
									elif return_tensors == "tf":
														if not is_tf_available():
																			raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""						)
														import tensorflow as tf

														def gen():
																			for ex in features:
																								yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

														_lowerCAmelCase         =       tf.data.Dataset.from_generator(
														    _UpperCAmelCase		,							({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,							({"""input_ids""": tf.TensorShape([None]						), """attention_mask""": tf.TensorShape([None]						)}, tf.TensorShape([]						))		,							)
														return dataset
									elif return_tensors == "pt":
														if not is_torch_available():
																			raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""						)
														import torch
														from torch.utils.data import TensorDataset

														_lowerCAmelCase         =       torch.tensor([f.input_ids for f in features]		,							dtype=torch.long						)
														_lowerCAmelCase         =       torch.tensor([f.attention_mask for f in features]		,							dtype=torch.long						)
														if self.mode == "classification":
																			_lowerCAmelCase         =       torch.tensor([f.label for f in features]		,							dtype=torch.long						)
														elif self.mode == "regression":
																			_lowerCAmelCase         =       torch.tensor([f.label for f in features]		,							dtype=torch.float						)

														_lowerCAmelCase         =       TensorDataset(_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase						)
														return dataset
									else:
														raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""						)











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=None							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]							, _UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]							, _UpperCAmelCase=True							, )		->				List[str]:
        lowerCamelCase									=size if size is not None else {"""height""": 20, """width""": 20}
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_resize
        lowerCamelCase									=size
        lowerCamelCase									=do_normalize
        lowerCamelCase									=image_mean
        lowerCamelCase									=image_std
        lowerCamelCase									=do_pad
        lowerCamelCase									=do_convert_rgb





     def 						lowerCamelCase							(	self  )		->				str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				Optional[int]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    str 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=BlipImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





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





     def 						lowerCamelCase							(	self  )		->				int:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Any 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=BlipImageProcessingTester(self							, num_channels=4  )
        lowerCamelCase									=3





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        pass





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )