



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 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  ["image_processor"]
       UpperCAmelCase__				:					Tuple        =  "SamImageProcessor"
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Dict:
          super().__init__(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	self.image_processor
          UpperCamelCase    :  List[str]							=	-10
          UpperCamelCase    :  Tuple							=	self.image_processor.size['longest_edge']
       def __call__(			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_ = None,			**SCREAMING_SNAKE_CASE_,			)     ->		BatchEncoding:
          UpperCamelCase    :  Any							=	self.image_processor(
              SCREAMING_SNAKE_CASE_,			return_tensors=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)

          # pop arguments that are not used in the foward but used nevertheless
          UpperCamelCase    :  Optional[Any]							=	encoding_image_processor['original_sizes']

          if hasattr(SCREAMING_SNAKE_CASE_,			'numpy'       ):  # Checks if Torch or TF tensor
             UpperCamelCase    :  int							=	original_sizes.numpy()

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	self._check_and_preprocess_points(
              input_points=SCREAMING_SNAKE_CASE_,			input_labels=SCREAMING_SNAKE_CASE_,			input_boxes=SCREAMING_SNAKE_CASE_,			)

          UpperCamelCase    :  int							=	self._normalize_and_convert(
              SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			input_points=SCREAMING_SNAKE_CASE_,			input_labels=SCREAMING_SNAKE_CASE_,			input_boxes=SCREAMING_SNAKE_CASE_,			return_tensors=SCREAMING_SNAKE_CASE_,			)

          return encoding_image_processor
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="pt",			)     ->		Any:
          if input_points is not None:
             if len(SCREAMING_SNAKE_CASE_       ) != len(SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  str							=	[
                    self._normalize_coordinates(self.target_size,			SCREAMING_SNAKE_CASE_,			original_sizes[0]       ) for point in input_points
                ]
             else:
                UpperCamelCase    :  int							=	[
                    self._normalize_coordinates(self.target_size,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
                    for point, original_size in zip(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
                ]
             # 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    :  Dict							=	self._pad_points_and_labels(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             UpperCamelCase    :  Any							=	np.array(SCREAMING_SNAKE_CASE_       )

          if input_labels is not None:
             UpperCamelCase    :  List[str]							=	np.array(SCREAMING_SNAKE_CASE_       )

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

          if input_boxes is not None:
             if return_tensors == "pt":
                UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       )
                # boxes batch size of 1 by default
                UpperCamelCase    :  Optional[Any]							=	input_boxes.unsqueeze(1       ) if len(input_boxes.shape       ) != 3 else input_boxes
             elif return_tensors == "tf":
                UpperCamelCase    :  int							=	tf.convert_to_tensor(SCREAMING_SNAKE_CASE_       )
                # boxes batch size of 1 by default
                UpperCamelCase    :  Tuple							=	tf.expand_dims(SCREAMING_SNAKE_CASE_,			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    :  Tuple							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       )
                # point batch size of 1 by default
                UpperCamelCase    :  Optional[Any]							=	input_points.unsqueeze(1       ) if len(input_points.shape       ) != 4 else input_points
             elif return_tensors == "tf":
                UpperCamelCase    :  List[str]							=	tf.convert_to_tensor(SCREAMING_SNAKE_CASE_       )
                # point batch size of 1 by default
                UpperCamelCase    :  Any							=	tf.expand_dims(SCREAMING_SNAKE_CASE_,			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    :  Union[str, Any]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       )
                # point batch size of 1 by default
                UpperCamelCase    :  Any							=	input_labels.unsqueeze(1       ) if len(input_labels.shape       ) != 3 else input_labels
             elif return_tensors == "tf":
                UpperCamelCase    :  int							=	tf.convert_to_tensor(SCREAMING_SNAKE_CASE_       )
                # point batch size of 1 by default
                UpperCamelCase    :  Dict							=	tf.expand_dims(SCREAMING_SNAKE_CASE_,			1       ) if len(input_labels.shape       ) != 3 else input_labels
             encoding_image_processor.update({'input_labels': input_labels}       )

          return encoding_image_processor
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  str							=	max([point.shape[0] for point in input_points]       )
          UpperCamelCase    :  List[Any]							=	[]
          for i, point in enumerate(SCREAMING_SNAKE_CASE_       ):
             if point.shape[0] != expected_nb_points:
                UpperCamelCase    :  Dict							=	np.concatenate(
                    [point, np.zeros((expected_nb_points - point.shape[0], 2)       ) + self.point_pad_value],			axis=0       )
                UpperCamelCase    :  List[Any]							=	np.append(input_labels[i],			[self.point_pad_value]       )
             processed_input_points.append(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	processed_input_points
          return input_points, input_labels
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=False       )     ->		np.ndarray:
          UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	original_size
          UpperCamelCase	,     UpperCamelCase    :  Dict							=	self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE_,			longest_edge=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	deepcopy(SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

          if is_bounding_box:
             UpperCamelCase    :  Optional[int]							=	coords.reshape(-1,			2,			2       )

          UpperCamelCase    :  str							=	coords[..., 0] * (new_w / old_w)
          UpperCamelCase    :  Optional[int]							=	coords[..., 1] * (new_h / old_h)

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

          return coords
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			)     ->		List[str]:
          if input_points is not None:
             if hasattr(SCREAMING_SNAKE_CASE_,			'numpy'       ):  # Checks for TF or Torch tensor
                UpperCamelCase    :  List[Any]							=	input_points.numpy().tolist()

             if not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) or not isinstance(input_points[0],			SCREAMING_SNAKE_CASE_       ):
                raise ValueError('Input points must be a list of list of floating points.'       )
             UpperCamelCase    :  Union[str, Any]							=	[np.array(SCREAMING_SNAKE_CASE_       ) for input_point in input_points]
          else:
             UpperCamelCase    :  Any							=	None

          if input_labels is not None:
             if hasattr(SCREAMING_SNAKE_CASE_,			'numpy'       ):
                UpperCamelCase    :  Union[str, Any]							=	input_labels.numpy().tolist()

             if not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) or not isinstance(input_labels[0],			SCREAMING_SNAKE_CASE_       ):
                raise ValueError('Input labels must be a list of list integers.'       )
             UpperCamelCase    :  Any							=	[np.array(SCREAMING_SNAKE_CASE_       ) for label in input_labels]
          else:
             UpperCamelCase    :  Optional[Any]							=	None

          if input_boxes is not None:
             if hasattr(SCREAMING_SNAKE_CASE_,			'numpy'       ):
                UpperCamelCase    :  Optional[int]							=	input_boxes.numpy().tolist()

             if (
                 not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
                 or not isinstance(input_boxes[0],			SCREAMING_SNAKE_CASE_       )
                 or not isinstance(input_boxes[0][0],			SCREAMING_SNAKE_CASE_       )
             ):
                raise ValueError('Input boxes must be a list of list of list of floating points.'       )
             UpperCamelCase    :  str							=	[np.array(SCREAMING_SNAKE_CASE_       ).astype(np.floataa       ) for box in input_boxes]
          else:
             UpperCamelCase    :  Optional[Any]							=	None

          return input_points, input_labels, input_boxes
       @property
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	self.image_processor.model_input_names
          return list(dict.fromkeys(SCREAMING_SNAKE_CASE_       )       )






       def 					snake_case_       (			self,			*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		str:
          return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          if torch_device == "cuda":
             model.half()
          return model






       @parameterized.expand(
           [
               ['en-ru', 26.0],
               ['ru-en', 22.0],
               ['en-de', 22.0],
               ['de-en', 29.0],
           ]       )
       @slow
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



