
"""simple docstring"""



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


UpperCAmelCase__          = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')

@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					PegasusTokenizer
     __a						=					PegasusTokenizerFast
     __a						=					True
     __a						=					True







     def      lowercase	(							self			:			str			):
       super().setUp()

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







     @cached_property
     def      lowercase	(							self			:			Any			):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large'''			)







     def      lowercase	(							self			:			Dict    ,      **_lowerCamelCase			:			Dict			):
       return PegasusTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]			):
       return ("This is a test", "This is a test")







     def      lowercase	(							self			:			str			):
       _snake_case					     =       '''</s>'''
       _snake_case					     =       1

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







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       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(_lowerCamelCase			)    ,      1103			)







     def      lowercase	(							self			:			Optional[int]			):
       self.assertEqual(self.get_tokenizer().vocab_size    ,      1103			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
       _snake_case					     =       self.tokenizer_class.from_pretrained(self.tmpdirname			)
       _snake_case					     =       (
           '''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>'''
       )
       _snake_case					     =       rust_tokenizer([raw_input_str]    ,      return_tensors=_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			).input_ids[0]
       _snake_case					     =       py_tokenizer([raw_input_str]    ,      return_tensors=_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			).input_ids[0]
       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       _snake_case					     =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       _snake_case					     =       [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
       _snake_case					     =       tokenizer([raw_input_str]    ,      return_tensors=_lowerCamelCase			).input_ids[0]
       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96103
       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
       _snake_case					     =       '''To ensure a smooth flow of bank resolutions.'''
       _snake_case					     =       [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
       _snake_case					     =       tokenizer([raw_input_str]    ,      return_tensors=_lowerCamelCase			).input_ids[0]
       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







     @require_torch
     def      lowercase	(							self			:			int			):
       _snake_case					     =       ['''This is going to be way too long.''' * 150, '''short example''']
       _snake_case					     =       ['''not super long but more than 5 tokens''', '''tiny''']
       _snake_case					     =       self._large_tokenizer(_lowerCamelCase    ,      padding=_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      return_tensors='''pt'''			)
       _snake_case					     =       self._large_tokenizer(
           text_target=_lowerCamelCase    ,      max_length=5    ,      padding=_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      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(_lowerCamelCase			) == 2  # input_ids, attention_mask.







     @slow
     def      lowercase	(							self			:			List[Any]			):
       # fmt: off
       _snake_case					     =       {'''input_ids''': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 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, 18289, 17780, 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=_lowerCamelCase    ,      model_name='''google/bigbird-pegasus-large-arxiv'''    ,      revision='''ba85d0851d708441f91440d509690f1ab6353415'''    ,      )






@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					PegasusTokenizer
     __a						=					PegasusTokenizerFast
     __a						=					True
     __a						=					True







     def      lowercase	(							self			:			Optional[Any]			):
       super().setUp()

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







     @cached_property
     def      lowercase	(							self			:			Optional[Any]			):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''			)







     def      lowercase	(							self			:			Any    ,      **_lowerCamelCase			:			int			):
       return PegasusTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Dict			):
       return ("This is a test", "This is a test")







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







     @require_torch
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       ['''This is going to be way too long.''' * 1000, '''short example''']
       _snake_case					     =       ['''not super long but more than 5 tokens''', '''tiny''']
       _snake_case					     =       self._large_tokenizer(_lowerCamelCase    ,      padding=_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      return_tensors='''pt'''			)
       _snake_case					     =       self._large_tokenizer(
           text_target=_lowerCamelCase    ,      max_length=5    ,      padding=_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      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(_lowerCamelCase			) == 2  # input_ids, attention_mask.







     def      lowercase	(							self			:			int			):
       _snake_case					     =       (
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       _snake_case					     =       self._large_tokenizer(_lowerCamelCase			).input_ids

       self.assertListEqual(
           _lowerCamelCase    ,      [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]    ,      )





"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class       lowerCAmelCase__				:







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			List[str]=32    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			Dict=10    ,      _lowerCamelCase			:			Tuple=[10, 20, 30, 40]    ,      _lowerCamelCase			:			int=[1, 1, 2, 1]    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Optional[int]=True    ,      _lowerCamelCase			:			Optional[int]="relu"    ,      _lowerCamelCase			:			List[Any]=3    ,      _lowerCamelCase			:			Dict=None    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       num_channels
       _snake_case					     =       embeddings_size
       _snake_case					     =       hidden_sizes
       _snake_case					     =       depths
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_act
       _snake_case					     =       num_labels
       _snake_case					     =       scope
       _snake_case					     =       len(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _snake_case					     =       None
       if self.use_labels:
         _snake_case					     =       ids_tensor([self.batch_size]    ,      self.num_labels			)

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Tuple			):
       return ResNetConfig(
           num_channels=self.num_channels    ,      embeddings_size=self.embeddings_size    ,      hidden_sizes=self.hidden_sizes    ,      depths=self.depths    ,      hidden_act=self.hidden_act    ,      num_labels=self.num_labels    ,      image_size=self.image_size    ,      )







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       TFResNetModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,      )







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.num_labels
       _snake_case					     =       TFResNetForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







     def      lowercase	(							self			:			List[Any]			):
       return







     @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			int			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       inspect.signature(model.call			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _snake_case					     =       [*signature.parameters.keys()]

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			str			):
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       model(**self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)			)

         _snake_case					     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _snake_case					     =       self.model_tester.num_stages
         self.assertEqual(len(_lowerCamelCase			)    ,      expected_num_stages + 1			)

         # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]			)    ,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,      )

       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       _snake_case					     =       ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
         for layer_type in layers_type:
           _snake_case					     =       layer_type
           _snake_case					     =       True
           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _snake_case					     =       True

           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFResNetModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

@require_tf
@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,      _lowerCamelCase    ,      atol=1e-4			)			)



