
"""simple docstring"""




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=7 ,			__UpperCamelCase=3 ,			__UpperCamelCase=18 ,			__UpperCamelCase=30 ,			__UpperCamelCase=4_00 ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			)->					Union[str, Any]:
          UpperCAmelCase__							: str 							=						size if size is not None else {"height": 18, "width": 18}
          UpperCAmelCase__							: Optional[Any] 							=						parent
          UpperCAmelCase__							: int 							=						batch_size
          UpperCAmelCase__							: Optional[Any] 							=						num_channels
          UpperCAmelCase__							: Dict 							=						image_size
          UpperCAmelCase__							: str 							=						min_resolution
          UpperCAmelCase__							: Optional[int] 							=						max_resolution
          UpperCAmelCase__							: List[Any] 							=						do_resize
          UpperCAmelCase__							: Dict 							=						size
          UpperCAmelCase__							: Optional[Any] 							=						do_normalize
          UpperCAmelCase__							: int 							=						image_mean
          UpperCAmelCase__							: int 							=						image_std



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class   _lowercase						(       lowercase__	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							DPTImageProcessor if is_vision_available() else None



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Any 							=						DPTImageProcessingTester(self  )



     @property
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(__lowercase ,			"image_mean"  )  )
          self.assertTrue(hasattr(__lowercase ,			"image_std"  )  )
          self.assertTrue(hasattr(__lowercase ,			"do_normalize"  )  )
          self.assertTrue(hasattr(__lowercase ,			"do_resize"  )  )
          self.assertTrue(hasattr(__lowercase ,			"size"  )  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Optional[int] 							=						self.image_processing_class.from_dict(self.image_processor_dict  )
          self.assertEqual(image_processor.size ,			{"height": 18, "width": 18}  )

          UpperCAmelCase__							: Dict 							=						self.image_processing_class.from_dict(self.image_processor_dict ,			size=42  )
          self.assertEqual(image_processor.size ,			{"height": 42, "width": 42}  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase__							: List[str] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          UpperCAmelCase__							: Dict 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__lowercase  )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,			Image.Image  )

          # Test not batched input
          UpperCAmelCase__							: Dict 							=						image_processing(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
          UpperCAmelCase__							: Union[str, Any] 							=						image_processing(__lowercase ,			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[int]:
          # Initialize image_processing
          UpperCAmelCase__							: int 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__							: Optional[Any] 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__lowercase ,			numpify=__lowercase  )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,			np.ndarray  )

          # Test not batched input
          UpperCAmelCase__							: Optional[Any] 							=						image_processing(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
          UpperCAmelCase__							: Union[str, Any] 							=						image_processing(__lowercase ,			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  )->					int:
          # Initialize image_processing
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__							: Any 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__lowercase ,			torchify=__lowercase  )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,			torch.Tensor  )

          # Test not batched input
          UpperCAmelCase__							: Optional[int] 							=						image_processing(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
          UpperCAmelCase__							: Union[str, Any] 							=						image_processing(__lowercase ,			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"],
              ) ,			)





"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors="pt" if labels is None else None ,			)

          if labels is None:
               return batch

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



