


'''simple docstring'''





import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 							UpperCAmelCase ( unittest.TestCase	):


 def __init__(			self       :  str ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  str=3 ,					__lowerCamelCase       :  Any=3_2 ,					__lowerCamelCase       :  str=3 ,					__lowerCamelCase       :  Dict=1_0 ,					__lowerCamelCase       :  int=[1_0, 2_0, 3_0, 4_0] ,					__lowerCamelCase       :  List[str]=[1, 1, 2, 1] ,					__lowerCamelCase       :  Optional[int]=True ,					__lowerCamelCase       :  Tuple=True ,					__lowerCamelCase       :  List[Any]="relu" ,					__lowerCamelCase       :  Optional[int]=3 ,					__lowerCamelCase       :  Optional[int]=None ,					):
    UpperCAmelCase__  :List[str]            =			parent
    UpperCAmelCase__  :Dict            =			batch_size
    UpperCAmelCase__  :Dict            =			image_size
    UpperCAmelCase__  :Tuple            =			num_channels
    UpperCAmelCase__  :Union[str, Any]            =			embeddings_size
    UpperCAmelCase__  :int            =			hidden_sizes
    UpperCAmelCase__  :Optional[int]            =			depths
    UpperCAmelCase__  :Optional[int]            =			is_training
    UpperCAmelCase__  :List[str]            =			use_labels
    UpperCAmelCase__  :Optional[Any]            =			hidden_act
    UpperCAmelCase__  :Union[str, Any]            =			num_labels
    UpperCAmelCase__  :Optional[int]            =			scope
    UpperCAmelCase__  :Optional[Any]            =			len(__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :str            =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    UpperCAmelCase__  :Optional[int]            =			self.get_config()

    return config, pixel_values


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    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 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any] ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  List[Any]   ):
    UpperCAmelCase__  :Optional[int]            =			FlaxResNetModel(config=__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			model(__lowerCamelCase   )

    # Output shape (b, c, h, w)
    self.parent.assertEqual(
        result.last_hidden_state.shape ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,					)


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  Optional[Any]   ):
    UpperCAmelCase__  :Any            =			self.num_labels
    UpperCAmelCase__  :Optional[int]            =			FlaxResNetForImageClassification(config=__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			model(__lowerCamelCase   )
    self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Optional[int]            =			self.prepare_config_and_inputs()
    UpperCAmelCase__      ,	UpperCAmelCase__  :Optional[int]            =			config_and_inputs
    UpperCAmelCase__  :List[Any]            =			{'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_flax
class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    UpperCAmelCase__  :List[str]            =			FlaxResNetModelTester(self   )
    UpperCAmelCase__  :Dict            =			ConfigTester(self ,					config_class=__lowerCamelCase ,					has_text_modality=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			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 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    return


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :int            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )


 @unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    pass


 @unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    pass


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCAmelCase__  :str            =			model_class(__lowerCamelCase   )
       UpperCAmelCase__  :List[str]            =			inspect.signature(model.__call__   )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       UpperCAmelCase__  :Union[str, Any]            =			[*signature.parameters.keys()]

       UpperCAmelCase__  :Optional[int]            =			['''pixel_values''']
       self.assertListEqual(arg_names[:1] ,					__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    def check_hidden_states_output(__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  int   ):
       UpperCAmelCase__  :Dict            =			model_class(__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			model(**self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )   )

       UpperCAmelCase__  :Optional[int]            =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       UpperCAmelCase__  :str            =			self.model_tester.num_stages
       self.assertEqual(len(__lowerCamelCase   ) ,					expected_num_stages + 1   )


 @unittest.skip(reason='''ResNet does not use feedforward chunking'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    pass





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :List[Any]            =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
          UpperCAmelCase__  :int            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
          UpperCAmelCase__  :List[str]            =			model_class(__lowerCamelCase   )

          @jax.jit
          def model_jitted(__lowerCamelCase       :  int ,					**__lowerCamelCase       :  Dict   ):
             return model(pixel_values=__lowerCamelCase ,					**__lowerCamelCase   )

          with self.subTest('''JIT Enabled'''   ):
             UpperCAmelCase__  :Dict            =			model_jitted(**__lowerCamelCase   ).to_tuple()

          with self.subTest('''JIT Disabled'''   ):
             with jax.disable_jit():
                UpperCAmelCase__  :Union[str, Any]            =			model_jitted(**__lowerCamelCase   ).to_tuple()

          self.assertEqual(len(__lowerCamelCase   ) ,					len(__lowerCamelCase   )   )
          for jitted_output, output in zip(__lowerCamelCase ,					__lowerCamelCase   ):
             self.assertEqual(jitted_output.shape ,					output.shape   )


def 					a__    (				):
   UpperCAmelCase__  :Tuple            =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
   return image




@require_flax
class 							UpperCAmelCase ( unittest.TestCase	):


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''   ) if is_vision_available() else None





 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    UpperCAmelCase__  :Union[str, Any]            =			FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''   )

    UpperCAmelCase__  :List[Any]            =			self.default_image_processor
    UpperCAmelCase__  :List[Any]            =			prepare_img()
    UpperCAmelCase__  :Optional[int]            =			image_processor(images=__lowerCamelCase ,					return_tensors='''np'''   )

    UpperCAmelCase__  :List[str]            =			model(**__lowerCamelCase   )

    # verify the logits
    UpperCAmelCase__  :Union[str, Any]            =			(1, 1_0_0_0)
    self.assertEqual(outputs.logits.shape ,					__lowerCamelCase   )

    UpperCAmelCase__  :int            =			jnp.array([-11.10_69, -9.78_77, -8.37_77]   )

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







'''simple docstring'''





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCamelCase       				=	'''stas/tiny-wmt19-en-ru'''




class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				FSMTTokenizer
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase__  :Any            =			[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    UpperCAmelCase__  :Dict            =			dict(zip(__lowerCamelCase ,					range(len(__lowerCamelCase   )   )   )   )
    UpperCAmelCase__  :Dict            =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    UpperCAmelCase__  :Dict            =			['''en''', '''ru''']
    UpperCAmelCase__  :Tuple            =			{
        '''langs''': self.langs,
        '''src_vocab_size''': 1_0,
        '''tgt_vocab_size''': 2_0,
    }

    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''src_vocab_file''']   )
    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
    UpperCAmelCase__  :Dict            =			os.path.join(self.tmpdirname ,					'''tokenizer_config.json'''   )
    UpperCAmelCase__  :Union[str, Any]            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']   )
    with open(self.src_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.tgt_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.merges_file ,					'''w'''   ) as fp:
       fp.write('''\n'''.join(__lowerCamelCase   )   )
    with open(__lowerCamelCase ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			FSMTTokenizer.from_pretrained(__lowerCamelCase   )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,					['''en''', '''ru''']   )
    self.assertEqual(tokenizer.src_vocab_size ,					2_1   )
    self.assertEqual(tokenizer.tgt_vocab_size ,					2_1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file   )

    UpperCAmelCase__  :Any            =			'''lower'''
    UpperCAmelCase__  :List[Any]            =			['''low''', '''er</w>''']
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.tokenize(__lowerCamelCase   )
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

    UpperCAmelCase__  :Dict            =			tokens + ['''<unk>''']
    UpperCAmelCase__  :Union[str, Any]            =			[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   ) ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :str            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.encode('''sequence builders''' ,					add_special_tokens=__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__lowerCamelCase   )

    UpperCAmelCase__  :Any            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,					__lowerCamelCase   )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :List[str]            =			self.tokenizer_en_ru
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Any            =			[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
       UpperCAmelCase__  :List[Any]            =			tokenizer_enc.encode(__lowerCamelCase ,					return_tensors=__lowerCamelCase   )
       self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

       # and decode backward, using the reversed languages model
       UpperCAmelCase__  :str            =			tokenizer_dec.decode(__lowerCamelCase ,					skip_special_tokens=__lowerCamelCase   )
       self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' ,					do_lower_case=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			tokenizer.tokenize('''USA is United States of America'''   )
    UpperCAmelCase__  :str            =			['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    pass





 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    pass



