

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""image_processor"""]
 UpperCAmelCase							    =						"""SamImageProcessor"""


 def __init__(						self		: List[Any]			,						lowerCAmelCase__		: int    )    ->      Dict:
   super().__init__(lowerCAmelCase__    )
   UpperCAmelCase					      =  self.image_processor
   UpperCAmelCase					      =  -1_0
   UpperCAmelCase					      =  self.image_processor.size["longest_edge"]


 def __call__(						self		: Dict			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: Union[str, Any]=None			,						lowerCAmelCase__		: Optional[Union[str, TensorType]] = None			,						**lowerCAmelCase__		: Optional[Any]			,						)    ->      BatchEncoding:
   UpperCAmelCase					      =  self.image_processor(
       lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

   # pop arguments that are not used in the foward but used nevertheless
   UpperCAmelCase					      =  encoding_image_processor["original_sizes"]

   if hasattr(lowerCAmelCase__			,						"numpy"    ):  # Checks if Torch or TF tensor
     UpperCAmelCase					      =  original_sizes.numpy()

   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  self._check_and_preprocess_points(
       input_points=lowerCAmelCase__			,						input_labels=lowerCAmelCase__			,						input_boxes=lowerCAmelCase__			,						)

   UpperCAmelCase					      =  self._normalize_and_convert(
       lowerCAmelCase__			,						lowerCAmelCase__			,						input_points=lowerCAmelCase__			,						input_labels=lowerCAmelCase__			,						input_boxes=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						)

   return encoding_image_processor


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: Optional[int]=None			,						lowerCAmelCase__		: Optional[Any]="pt"			,						)    ->      Optional[int]:
   if input_points is not None:
     if len(lowerCAmelCase__    ) != len(lowerCAmelCase__    ):
       UpperCAmelCase					      =  [
           self._normalize_coordinates(self.target_size			,						lowerCAmelCase__			,						original_sizes[0]    ) for point in input_points
       ]
     else:
       UpperCAmelCase					      =  [
           self._normalize_coordinates(self.target_size			,						lowerCAmelCase__			,						lowerCAmelCase__    )
           for point, original_size in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
       ]
     # check that all arrays have the same shape
     if not all(point.shape == input_points[0].shape for point in input_points    ):
       if input_labels is not None:
         UpperCAmelCase      ,	UpperCAmelCase					      =  self._pad_points_and_labels(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  np.array(lowerCAmelCase__    )

   if input_labels is not None:
     UpperCAmelCase					      =  np.array(lowerCAmelCase__    )

   if input_boxes is not None:
     if len(lowerCAmelCase__    ) != len(lowerCAmelCase__    ):
       UpperCAmelCase					      =  [
           self._normalize_coordinates(self.target_size			,						lowerCAmelCase__			,						original_sizes[0]			,						is_bounding_box=lowerCAmelCase__    )
           for box in input_boxes
       ]
     else:
       UpperCAmelCase					      =  [
           self._normalize_coordinates(self.target_size			,						lowerCAmelCase__			,						lowerCAmelCase__			,						is_bounding_box=lowerCAmelCase__    )
           for box, original_size in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
       ]
     UpperCAmelCase					      =  np.array(lowerCAmelCase__    )

   if input_boxes is not None:
     if return_tensors == "pt":
       UpperCAmelCase					      =  torch.from_numpy(lowerCAmelCase__    )
       # boxes batch size of 1 by default
       UpperCAmelCase					      =  input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
     elif return_tensors == "tf":
       UpperCAmelCase					      =  tf.convert_to_tensor(lowerCAmelCase__    )
       # boxes batch size of 1 by default
       UpperCAmelCase					      =  tf.expand_dims(lowerCAmelCase__			,						1    ) if len(input_boxes.shape    ) != 3 else input_boxes
     encoding_image_processor.update({"input_boxes": input_boxes}    )
   if input_points is not None:
     if return_tensors == "pt":
       UpperCAmelCase					      =  torch.from_numpy(lowerCAmelCase__    )
       # point batch size of 1 by default
       UpperCAmelCase					      =  input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
     elif return_tensors == "tf":
       UpperCAmelCase					      =  tf.convert_to_tensor(lowerCAmelCase__    )
       # point batch size of 1 by default
       UpperCAmelCase					      =  tf.expand_dims(lowerCAmelCase__			,						1    ) if len(input_points.shape    ) != 4 else input_points
     encoding_image_processor.update({"input_points": input_points}    )
   if input_labels is not None:
     if return_tensors == "pt":
       UpperCAmelCase					      =  torch.from_numpy(lowerCAmelCase__    )
       # point batch size of 1 by default
       UpperCAmelCase					      =  input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
     elif return_tensors == "tf":
       UpperCAmelCase					      =  tf.convert_to_tensor(lowerCAmelCase__    )
       # point batch size of 1 by default
       UpperCAmelCase					      =  tf.expand_dims(lowerCAmelCase__			,						1    ) if len(input_labels.shape    ) != 3 else input_labels
     encoding_image_processor.update({"input_labels": input_labels}    )

   return encoding_image_processor


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Any    )    ->      Any:
   UpperCAmelCase					      =  max([point.shape[0] for point in input_points]    )
   UpperCAmelCase					      =  []
   for i, point in enumerate(lowerCAmelCase__    ):
     if point.shape[0] != expected_nb_points:
       UpperCAmelCase					      =  np.concatenate(
           [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]			,						axis=0    )
       UpperCAmelCase					      =  np.append(input_labels[i]			,						[self.point_pad_value]    )
     processed_input_points.append(lowerCAmelCase__    )
   UpperCAmelCase					      =  processed_input_points
   return input_points, input_labels


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Any=False    )    ->      np.ndarray:
   UpperCAmelCase      ,	UpperCAmelCase					      =  original_size
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor._get_preprocess_shape(lowerCAmelCase__			,						longest_edge=lowerCAmelCase__    )
   UpperCAmelCase					      =  deepcopy(lowerCAmelCase__    ).astype(lowerCAmelCase__    )

   if is_bounding_box:
     UpperCAmelCase					      =  coords.reshape(-1			,						2			,						2    )

   UpperCAmelCase					      =  coords[..., 0] * (new_w / old_w)
   UpperCAmelCase					      =  coords[..., 1] * (new_h / old_h)

   if is_bounding_box:
     UpperCAmelCase					      =  coords.reshape(-1			,						4    )

   return coords


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: Any=None			,						lowerCAmelCase__		: Optional[int]=None			,						)    ->      int:
   if input_points is not None:
     if hasattr(lowerCAmelCase__			,						"numpy"    ):  # Checks for TF or Torch tensor
       UpperCAmelCase					      =  input_points.numpy().tolist()

     if not isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) or not isinstance(input_points[0]			,						lowerCAmelCase__    ):
       raise ValueError("Input points must be a list of list of floating points."    )
     UpperCAmelCase					      =  [np.array(lowerCAmelCase__    ) for input_point in input_points]
   else:
     UpperCAmelCase					      =  None

   if input_labels is not None:
     if hasattr(lowerCAmelCase__			,						"numpy"    ):
       UpperCAmelCase					      =  input_labels.numpy().tolist()

     if not isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) or not isinstance(input_labels[0]			,						lowerCAmelCase__    ):
       raise ValueError("Input labels must be a list of list integers."    )
     UpperCAmelCase					      =  [np.array(lowerCAmelCase__    ) for label in input_labels]
   else:
     UpperCAmelCase					      =  None

   if input_boxes is not None:
     if hasattr(lowerCAmelCase__			,						"numpy"    ):
       UpperCAmelCase					      =  input_boxes.numpy().tolist()

     if (
         not isinstance(lowerCAmelCase__			,						lowerCAmelCase__    )
         or not isinstance(input_boxes[0]			,						lowerCAmelCase__    )
         or not isinstance(input_boxes[0][0]			,						lowerCAmelCase__    )
     ):
       raise ValueError("Input boxes must be a list of list of list of floating points."    )
     UpperCAmelCase					      =  [np.array(lowerCAmelCase__    ).astype(np.floataa    ) for box in input_boxes]
   else:
     UpperCAmelCase					      =  None

   return input_points, input_labels, input_boxes


 @property
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   UpperCAmelCase					      =  self.image_processor.model_input_names
   return list(dict.fromkeys(lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: List[str]			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: Tuple    )    ->      List[Any]:
   return self.image_processor.post_process_masks(*lowerCAmelCase__			,						**lowerCAmelCase__    )






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						LEDTokenizer
 UpperCAmelCase							    =						LEDTokenizerFast
 UpperCAmelCase							    =						True


 def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
   super().setUp()
   UpperCAmelCase					      =  [
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   UpperCAmelCase					      =  dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__    )    )    )    )
   UpperCAmelCase					      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   UpperCAmelCase					      =  {"unk_token": "<unk>"}

   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["vocab_file"]    )
   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["merges_file"]    )
   with open(self.vocab_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write(json.dumps(lowerCAmelCase__    ) + "\n"    )
   with open(self.merges_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write("\n".join(lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						**lowerCAmelCase__		: Optional[int]    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: str			,						**lowerCAmelCase__		: str    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[Any]    )    ->      List[Any]:
   return "lower newer", "lower newer"


 @cached_property
 def _UpperCamelCase	(						self		: Dict    )    ->      str:
   return LEDTokenizer.from_pretrained("allenai/led-base-16384"    )


 @cached_property
 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"    )


 @require_torch
 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   UpperCAmelCase					      =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						max_length=len(lowerCAmelCase__    )			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )

     self.assertEqual((2, 9)			,						batch.input_ids.shape    )
     self.assertEqual((2, 9)			,						batch.attention_mask.shape    )
     UpperCAmelCase					      =  batch.input_ids.tolist()[0]
     self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIn("input_ids"			,						lowerCAmelCase__    )
     self.assertIn("attention_mask"			,						lowerCAmelCase__    )
     self.assertNotIn("labels"			,						lowerCAmelCase__    )
     self.assertNotIn("decoder_attention_mask"			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: int    )    ->      int:
   UpperCAmelCase					      =  [
       "Summary of the text.",
       "Another summary.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						max_length=3_2			,						padding="max_length"			,						return_tensors="pt"    )
     self.assertEqual(3_2			,						targets["input_ids"].shape[1]    )


 @require_torch
 def _UpperCamelCase	(						self		: Any    )    ->      int:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(
         ["I am a small frog" * 1_0_2_4, "I am a small frog"]			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )
     self.assertEqual(batch.input_ids.shape			,						(2, 5_1_2_2)    )


 @require_torch
 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  ["A long paragraph for summarization."]
   UpperCAmelCase					      =  [
       "Summary of the text.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  inputs["input_ids"]
     UpperCAmelCase					      =  targets["input_ids"]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )


 @require_torch
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[Any]:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  ["Summary of the text.", "Another summary."]
     UpperCAmelCase					      =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__    )
     UpperCAmelCase					      =  [[0] * len(lowerCAmelCase__    ) for x in encoded_output["input_ids"]]
     UpperCAmelCase					      =  tokenizer.pad(lowerCAmelCase__    )
     self.assertSequenceEqual(outputs["global_attention_mask"]			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   pass


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
       UpperCAmelCase					      =  self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  "A, <mask> AllenNLP sentence."
       UpperCAmelCase					      =  tokenizer_r.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       UpperCAmelCase					      =  tokenizer_p.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       self.assertEqual(sum(tokens_r["token_type_ids"]    )			,						sum(tokens_p["token_type_ids"]    )    )
       self.assertEqual(
           sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )			,						sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )			,						)

       UpperCAmelCase					      =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
       UpperCAmelCase					      =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )
       self.assertSequenceEqual(tokens_p["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
       self.assertSequenceEqual(tokens_r["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )



