






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
     from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DPTImageProcessor




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__=2      ,    a__=32      ,    a__=16      ,    a__=3      ,    a__=True      ,    a__=True      ,    a__=32      ,    a__=4      ,    a__=[0, 1, 2, 3]      ,    a__=4      ,    a__=37      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=0.02      ,    a__=3      ,    a__=[1, 3_84, 24, 24]      ,    a__=True      ,    a__=None      ,    )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    parent
            __snake_case      :List[str] 							=    batch_size
            __snake_case      :Optional[Any] 							=    image_size
            __snake_case      :Optional[int] 							=    patch_size
            __snake_case      :List[str] 							=    num_channels
            __snake_case      :List[Any] 							=    is_training
            __snake_case      :Optional[Any] 							=    use_labels
            __snake_case      :Dict 							=    hidden_size
            __snake_case      :Union[str, Any] 							=    num_hidden_layers
            __snake_case      :Union[str, Any] 							=    backbone_out_indices
            __snake_case      :int 							=    num_attention_heads
            __snake_case      :Any 							=    intermediate_size
            __snake_case      :Optional[Any] 							=    hidden_act
            __snake_case      :List[str] 							=    hidden_dropout_prob
            __snake_case      :Union[str, Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    initializer_range
            __snake_case      :List[Any] 							=    num_labels
            __snake_case      :int 							=    backbone_featmap_shape
            __snake_case      :Tuple 							=    scope
            __snake_case      :List[Any] 							=    is_hybrid
            # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
            __snake_case      :int 							=    (image_size // patch_size) ** 2
            __snake_case      :Union[str, Any] 							=    num_patches + 1



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

            __snake_case      :List[Any] 							=    None
            if self.use_labels:
                 __snake_case      :Union[str, Any] 							=    ids_tensor([self.batch_size, self.image_size, self.image_size]      ,    self.num_labels     )

            __snake_case      :List[str] 							=    self.get_config()

            return config, pixel_values, labels



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    {
                """global_padding""": """same""",
                """layer_type""": """bottleneck""",
                """depths""": [3, 4, 9],
                """out_features""": ["""stage1""", """stage2""", """stage3"""],
                """embedding_dynamic_padding""": True,
                """hidden_sizes""": [96, 1_92, 3_84, 7_68],
                """num_groups""": 2,
            }

            return DPTConfig(
                image_size=self.image_size      ,    patch_size=self.patch_size      ,    num_channels=self.num_channels      ,    hidden_size=self.hidden_size      ,    num_hidden_layers=self.num_hidden_layers      ,    backbone_out_indices=self.backbone_out_indices      ,    num_attention_heads=self.num_attention_heads      ,    intermediate_size=self.intermediate_size      ,    hidden_act=self.hidden_act      ,    hidden_dropout_prob=self.hidden_dropout_prob      ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,    is_decoder=a__      ,    initializer_range=self.initializer_range      ,    is_hybrid=self.is_hybrid      ,    backbone_config=a__      ,    backbone_featmap_shape=self.backbone_featmap_shape      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    DPTModel(config=a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :int 							=    model(a__     )
            self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    self.num_labels
            __snake_case      :Any 							=    DPTForDepthEstimation(a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :int 							=    model(a__     )
            self.parent.assertEqual(result.predicted_depth.shape      ,    (self.batch_size, self.image_size, self.image_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.num_labels
            __snake_case      :List[str] 							=    DPTForSemanticSegmentation(a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :Optional[int] 							=    model(a__      ,    labels=a__     )
            self.parent.assertEqual(
                result.logits.shape      ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.prepare_config_and_inputs()
            __snake_case							,			__snake_case							,			__snake_case      :Tuple 							=    config_and_inputs
            __snake_case      :Union[str, Any] 							=    {"""pixel_values""": pixel_values}
            return config, inputs_dict




@require_torch
class       snake_case__ (				lowercase_						,     lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					str								=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       lowerCamelCase				:					Optional[int]								=			(
           {
               "depth-estimation": DPTForDepthEstimation,
               "feature-extraction": DPTModel,
               "image-segmentation": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCamelCase				:					List[str]								=			False
       lowerCamelCase				:					Optional[int]								=			False
       lowerCamelCase				:					str								=			False



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






            '''simple docstring'''





            __snake_case      :Any 							=    DPTModelTester(self     )
            __snake_case      :Dict 							=    ConfigTester(self      ,    config_class=a__      ,    has_text_modality=a__      ,    hidden_size=37     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            self.config_tester.run_common_tests()



       @unittest.skip(reason="""DPT does not use inputs_embeds"""     )
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case							,			__snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case      :List[Any] 							=    model_class(a__     )
                 self.assertIsInstance(model.get_input_embeddings()      ,    (nn.Module)     )
                 __snake_case      :int 							=    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(a__      ,    nn.Linear     )     )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case							,			__snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case      :str 							=    model_class(a__     )
                 __snake_case      :str 							=    inspect.signature(model.forward     )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case      :Tuple 							=    [*signature.parameters.keys()]

                 __snake_case      :Optional[int] 							=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,    a__     )



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






            '''simple docstring'''





            __snake_case      :Any 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :int 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_depth_estimation(*a__     )



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






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*a__     )



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






            '''simple docstring'''





            for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                      continue

                 __snake_case							,			__snake_case      :str 							=    self.model_tester.prepare_config_and_inputs_for_common()
                 __snake_case      :Optional[Any] 							=    True

                 if model_class in get_values(a__     ):
                      continue

                 __snake_case      :int 							=    model_class(a__     )
                 model.to(a__     )
                 model.train()
                 __snake_case      :Optional[Any] 							=    self._prepare_for_class(a__      ,    a__      ,    return_labels=a__     )
                 __snake_case      :Optional[Any] 							=    model(**a__     ).loss
                 loss.backward()



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                      continue

                 __snake_case							,			__snake_case      :Any 							=    self.model_tester.prepare_config_and_inputs_for_common()
                 __snake_case      :List[Any] 							=    False
                 __snake_case      :Union[str, Any] 							=    True

                 if model_class in get_values(a__     ) or not model_class.supports_gradient_checkpointing:
                      continue
                 __snake_case      :str 							=    model_class(a__     )
                 model.to(a__     )
                 model.gradient_checkpointing_enable()
                 model.train()
                 __snake_case      :List[Any] 							=    self._prepare_for_class(a__      ,    a__      ,    return_labels=a__     )
                 __snake_case      :str 							=    model(**a__     ).loss
                 loss.backward()



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case							,			__snake_case      :int 							=    self.model_tester.prepare_config_and_inputs_for_common()

            __snake_case      :Union[str, Any] 							=    _config_zero_init(a__     )
            for model_class in self.all_model_classes:
                 __snake_case      :List[Any] 							=    model_class(config=a__     )
                 # Skip the check for the backbone
                 __snake_case      :str 							=    []
                 for name, module in model.named_modules():
                      if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                           __snake_case      :Union[str, Any] 							=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

                 for name, param in model.named_parameters():
                      if param.requires_grad:
                           if name in backbone_params:
                                continue
                           self.assertIn(
                               ((param.data.mean() * 1e9).round() / 1e9).item()      ,    [0.0, 1.0]      ,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,    )



       @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            pass



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                 __snake_case      :Optional[int] 							=    DPTModel.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case							,			__snake_case      :int 							=    self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case      :Dict 							=    """add"""
            with self.assertRaises(a__     ):
                 __snake_case      :Optional[int] 							=    DPTForDepthEstimation(a__     )






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
     return image




@require_torch
@require_vision
@slow
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



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






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""     )
            __snake_case      :Any 							=    DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""     ).to(a__     )

            __snake_case      :str 							=    prepare_img()
            __snake_case      :List[Any] 							=    image_processor(images=a__      ,    return_tensors="""pt"""     ).to(a__     )

            # forward pass
            with torch.no_grad():
                 __snake_case      :List[str] 							=    model(**a__     )
                 __snake_case      :List[str] 							=    outputs.predicted_depth

            # verify the predicted depth
            __snake_case      :Optional[int] 							=    torch.Size((1, 3_84, 3_84)     )
            self.assertEqual(predicted_depth.shape      ,    a__     )

            __snake_case      :Tuple 							=    torch.tensor(
                [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]     ).to(a__     )

            self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00      ,    a__      ,    atol=1e-4     )     )










import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			GPTaTokenizer
       lowerCamelCase				:					Optional[int]								=			GPTaTokenizerFast
       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					List[str]								=			{"add_prefix_space": True}
       lowerCamelCase				:					Optional[int]								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case      :Optional[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
                """<|endoftext|>""",
            ]
            __snake_case      :Dict 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :List[Any] 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :List[str] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Dict 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[Any] 							=    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(a__     ) + """\n"""     )
            with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(a__     )     )



       def        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :Any 							=    """lower newer"""
            return input_text, output_text



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






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    GPTaTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map     )
            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :int 							=    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __snake_case      :Any 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[Any] 							=    tokens + [tokenizer.unk_token]
            __snake_case      :List[Any] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()
            __snake_case      :Optional[Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )

            __snake_case      :Optional[int] 							=    """lower newer"""

            # Testing tokenization
            __snake_case      :List[Any] 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.tokenize(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids without special tokens
            __snake_case      :List[str] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.encode(a__      ,    add_special_tokens=a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids with special tokens
            __snake_case      :Union[str, Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    tokenizer.encode(a__      ,    add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    rust_tokenizer.encode(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing the unknown token
            __snake_case      :Dict 							=    tokens + [rust_tokenizer.unk_token]
            __snake_case      :Optional[int] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   Any:






            '''simple docstring'''





            pass



       def        __lowercase    (				self      ,    a__=15     )			->   Optional[Any]:






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Optional[int] 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )

                      # Simple input
                      __snake_case      :List[str] 							=    """This is a simple input"""
                      __snake_case      :List[str] 							=    ["""This is a simple input 1""", """This is a simple input 2"""]
                      __snake_case      :Tuple 							=    ("""This is a simple input""", """This is a pair""")
                      __snake_case      :Union[str, Any] 							=    [
                          ("""This is a simple input 1""", """This is a simple input 2"""),
                          ("""This is a simple pair 1""", """This is a simple pair 2"""),
                      ]

                      # Simple input tests
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    pad_token="""<pad>"""     )

            # Simple input
            __snake_case      :Tuple 							=    """This is a simple input"""
            __snake_case      :Optional[int] 							=    ["""This is a simple input looooooooong""", """This is a simple input"""]
            __snake_case      :List[str] 							=    ("""This is a simple input""", """This is a pair""")
            __snake_case      :List[str] 							=    [
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            __snake_case      :Tuple 							=    tokenizer.pad_token_id

            __snake_case      :int 							=    tokenizer(a__      ,    padding="""max_length"""      ,    max_length=30      ,    return_tensors="""np"""     )
            __snake_case      :Optional[Any] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )
            __snake_case      :List[Any] 							=    tokenizer(*a__      ,    padding="""max_length"""      ,    max_length=60      ,    return_tensors="""np"""     )
            __snake_case      :Optional[int] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )

            # s
            # test single string max_length padding
            self.assertEqual(out_s["""input_ids"""].shape[-1]      ,    30     )
            self.assertTrue(pad_token_id in out_s["""input_ids"""]     )
            self.assertTrue(0 in out_s["""attention_mask"""]     )

            # s2
            # test automatic padding
            self.assertEqual(out_sa["""input_ids"""].shape[-1]      ,    33     )
            # long slice doesn't have padding
            self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]     )
            self.assertFalse(0 in out_sa["""attention_mask"""][0]     )
            # short slice does have padding
            self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]     )
            self.assertTrue(0 in out_sa["""attention_mask"""][1]     )

            # p
            # test single pair max_length padding
            self.assertEqual(out_p["""input_ids"""].shape[-1]      ,    60     )
            self.assertTrue(pad_token_id in out_p["""input_ids"""]     )
            self.assertTrue(0 in out_p["""attention_mask"""]     )

            # p2
            # test automatic padding pair
            self.assertEqual(out_pa["""input_ids"""].shape[-1]      ,    52     )
            # long slice pair doesn't have padding
            self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]     )
            self.assertFalse(0 in out_pa["""attention_mask"""][0]     )
            # short slice pair does have padding
            self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]     )
            self.assertTrue(0 in out_pa["""attention_mask"""][1]     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    """$$$"""
            __snake_case      :Optional[Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    bos_token=a__      ,    add_bos_token=a__     )

            __snake_case      :List[Any] 							=    """This is a simple input"""
            __snake_case      :Tuple 							=    ["""This is a simple input 1""", """This is a simple input 2"""]

            __snake_case      :Tuple 							=    tokenizer.bos_token_id

            __snake_case      :List[str] 							=    tokenizer(a__     )
            __snake_case      :Any 							=    tokenizer(a__     )

            self.assertEqual(out_s.input_ids[0]      ,    a__     )
            self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

            __snake_case      :int 							=    tokenizer.decode(out_s.input_ids     )
            __snake_case      :Tuple 							=    tokenizer.batch_decode(out_sa.input_ids     )

            self.assertEqual(decode_s.split()[0]      ,    a__     )
            self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



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






            '''simple docstring'''





            __snake_case      :List[str] 							=    [self.get_tokenizer(do_lower_case=a__      ,    add_bos_token=a__     )]
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                      __snake_case      :Tuple 							=    """Encode this."""
                      __snake_case      :Tuple 							=    """This one too please."""
                      __snake_case      :Union[str, Any] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      encoded_sequence += tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      __snake_case      :List[str] 							=    tokenizer.encode_plus(
                          a__      ,    a__      ,    add_special_tokens=a__      ,    return_special_tokens_mask=a__      ,    )
                      __snake_case      :Union[str, Any] 							=    encoded_sequence_dict["""input_ids"""]
                      __snake_case      :Optional[Any] 							=    encoded_sequence_dict["""special_tokens_mask"""]
                      self.assertEqual(len(a__     )      ,    len(a__     )     )

                      __snake_case      :Optional[int] 							=    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__     )
                      ]
                      __snake_case      :str 							=    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(a__      ,    a__     )




@require_tokenizers
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )
            __snake_case      :Union[str, Any] 							=    """A photo of a cat"""

            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""test_opt"""     )

            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""./test_opt"""     )
            __snake_case      :Tuple 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    use_slow=a__     )
            __snake_case      :str 							=    """A photo of a cat"""

            __snake_case      :List[str] 							=    tokenizer.encode(
                a__      ,    )
            # Same as above
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""     )
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )

            __snake_case      :Optional[Any] 							=    """bos"""
            __snake_case      :int 							=    tokenizer.get_vocab()["""bos"""]

            __snake_case      :str 							=    """A photo of a cat"""
            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            # We changed the bos token
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""./tok"""     )
            __snake_case      :Dict 							=    AutoTokenizer.from_pretrained("""./tok"""     )
            self.assertTrue(tokenizer.is_fast     )
            __snake_case      :List[Any] 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


