


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A			:						Optional[Any]        =  get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					PegasusTokenizer
					A__     =					PegasusTokenizerFast
					A__     =					True
					A__     =					True

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Tuple:




							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase__	     =						PegasusTokenizer(_UpperCAmelCase						)
							tokenizer.save_pretrained(self.tmpdirname						)

					@cached_property
					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							return PegasusTokenizer.from_pretrained("""google/pegasus-large"""						)

					def     lowerCamelCase__	(self       :		Tuple ,   **_UpperCAmelCase       :		int						)		->  PegasusTokenizer:




							"""simple docstring"""


							return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Any						)		->  List[str]:




							"""simple docstring"""


							return ("This is a test", "This is a test")

					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						"""</s>"""
							lowercase__	     =						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						) ,   _UpperCAmelCase						)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						) ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						list(self.get_tokenizer().get_vocab().keys()						)

							self.assertEqual(vocab_keys[0] ,   """<pad>"""						)
							self.assertEqual(vocab_keys[1] ,   """</s>"""						)
							self.assertEqual(vocab_keys[-1] ,   """v"""						)
							self.assertEqual(len(_UpperCAmelCase						) ,   1103						)

					def     lowerCamelCase__	(self       :		str						)		->  Dict:




							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size ,   1103						)

					def     lowerCamelCase__	(self       :		Any						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
							lowercase__	     =						self.tokenizer_class.from_pretrained(self.tmpdirname						)
							lowercase__	     =						(
							    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
							    """ </s> <pad> <pad> <pad>"""
							)
							lowercase__	     =						rust_tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						).input_ids[0]
							lowercase__	     =						py_tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						).input_ids[0]
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		int						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowercase__	     =						"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
							lowercase__	     =						[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
							lowercase__	     =						tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase						).input_ids[0]
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 9_6103
							assert tokenizer.pad_token_id == 0
							assert tokenizer.eos_token_id == 1
							assert tokenizer.offset == 103
							assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
							assert tokenizer.unk_token == "<unk>"
							assert tokenizer.model_max_length == 1024
							lowercase__	     =						"""To ensure a smooth flow of bank resolutions."""
							lowercase__	     =						[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
							lowercase__	     =						tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase						).input_ids[0]
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]						) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

					@require_torch
					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						["""This is going to be way too long.""" * 150, """short example"""]
							lowercase__	     =						["""not super long but more than 5 tokens""", """tiny"""]
							lowercase__	     =						self._large_tokenizer(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   return_tensors="""pt"""						)
							lowercase__	     =						self._large_tokenizer(
							    text_target=_UpperCAmelCase ,   max_length=5 ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   return_tensors="""pt"""						)

							assert batch.input_ids.shape == (2, 1024)
							assert batch.attention_mask.shape == (2, 1024)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_UpperCAmelCase						) == 2  # input_ids, attention_mask.


					@slow
					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCAmelCase ,   model_name="""google/bigbird-pegasus-large-arxiv""" ,   revision="""ba85d0851d708441f91440d509690f1ab6353415""" ,   )






@require_sentencepiece
@require_tokenizers
class 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					PegasusTokenizer
					A__     =					PegasusTokenizerFast
					A__     =					True
					A__     =					True

					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase__	     =						PegasusTokenizer(_UpperCAmelCase ,   offset=0 ,   mask_token_sent=_UpperCAmelCase ,   mask_token="""[MASK]"""						)
							tokenizer.save_pretrained(self.tmpdirname						)

					@cached_property
					def     lowerCamelCase__	(self       :		Any						)		->  str:




							"""simple docstring"""


							return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""						)

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  PegasusTokenizer:




							"""simple docstring"""


							return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		Any						)		->  Union[str, Any]:




							"""simple docstring"""


							return ("This is a test", "This is a test")

					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
							lowercase__	     =						self.tokenizer_class.from_pretrained(self.tmpdirname						)
							lowercase__	     =						(
							    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
							    """ <pad> <pad> <pad>"""
							)
							lowercase__	     =						rust_tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						).input_ids[0]
							lowercase__	     =						py_tokenizer([raw_input_str] ,   return_tensors=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						).input_ids[0]
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

					@require_torch
					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						["""This is going to be way too long.""" * 1000, """short example"""]
							lowercase__	     =						["""not super long but more than 5 tokens""", """tiny"""]
							lowercase__	     =						self._large_tokenizer(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   return_tensors="""pt"""						)
							lowercase__	     =						self._large_tokenizer(
							    text_target=_UpperCAmelCase ,   max_length=5 ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   return_tensors="""pt"""						)

							assert batch.input_ids.shape == (2, 4096)
							assert batch.attention_mask.shape == (2, 4096)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_UpperCAmelCase						) == 2  # input_ids, attention_mask.


					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						(
							    """This is an example string that is used to test the original TF implementation against the HF"""
							    """ implementation"""
							)

							lowercase__	     =						self._large_tokenizer(_UpperCAmelCase						).input_ids

							self.assertListEqual(
							    _UpperCAmelCase ,   [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,   )



'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





