import inspect
import unittest

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

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


if is_torch_available():
 import torch

 from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
 from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''

 def     __snake_case    (	self				:						Any     )       ->							Optional[Any]:
        _UpperCamelCase   :				List[Any]       =     self.config_class(**self.inputs_dict     )
        self.parent.assertTrue(hasattr(lowerCamelCase				,	'hidden_sizes'     )     )
        self.parent.assertTrue(hasattr(lowerCamelCase				,	'num_encoder_blocks'     )     )







class       __lowerCAmelCase     :



 '''simple docstring'''

 def __init__(	self				:						List[Any]				,	lowerCamelCase				:						Any				,	lowerCamelCase				:						Union[str, Any]=1_3				,	lowerCamelCase				:						Dict=6_4				,	lowerCamelCase				:						List[str]=3				,	lowerCamelCase				:						Optional[int]=4				,	lowerCamelCase				:						Tuple=[2, 2, 2, 2]				,	lowerCamelCase				:						Tuple=[8, 4, 2, 1]				,	lowerCamelCase				:						Optional[int]=[1_6, 3_2, 6_4, 1_2_8]				,	lowerCamelCase				:						Any=[1, 4, 8, 1_6]				,	lowerCamelCase				:						Dict=False				,	lowerCamelCase				:						Union[str, Any]=True				,	lowerCamelCase				:						int="gelu"				,	lowerCamelCase				:						List[Any]=0.1				,	lowerCamelCase				:						Dict=0.0_2				,	lowerCamelCase				:						str=3				,	lowerCamelCase				:						str=None				,	)       ->							Optional[int]:
        _UpperCamelCase   :				str       =     parent
        _UpperCamelCase   :				int       =     batch_size
        _UpperCamelCase   :				Dict       =     image_size
        _UpperCamelCase   :				Any       =     num_channels
        _UpperCamelCase   :				int       =     num_encoder_blocks
        _UpperCamelCase   :				str       =     sr_ratios
        _UpperCamelCase   :				str       =     depths
        _UpperCamelCase   :				Dict       =     hidden_sizes
        _UpperCamelCase   :				Tuple       =     downsampling_rates
        _UpperCamelCase   :				List[Any]       =     is_training
        _UpperCamelCase   :				str       =     use_labels
        _UpperCamelCase   :				Tuple       =     hidden_act
        _UpperCamelCase   :				List[Any]       =     hidden_dropout_prob
        _UpperCamelCase   :				int       =     initializer_range
        _UpperCamelCase   :				Dict       =     num_labels
        _UpperCamelCase   :				Dict       =     scope

 def     __snake_case    (	self				:						Tuple     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        _UpperCamelCase   :				Tuple       =     None
        if self.use_labels:
               _UpperCamelCase   :				Dict       =     ids_tensor([self.batch_size, self.image_size, self.image_size]				,	self.num_labels     )

        _UpperCamelCase   :				Union[str, Any]       =     PoolFormerConfig(
            image_size=self.image_size				,	num_channels=self.num_channels				,	num_encoder_blocks=self.num_encoder_blocks				,	depths=self.depths				,	hidden_sizes=self.hidden_sizes				,	hidden_act=self.hidden_act				,	hidden_dropout_prob=self.hidden_dropout_prob				,	initializer_range=self.initializer_range				,	)

        return config, pixel_values, labels

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						int				,	lowerCamelCase				:						Union[str, Any]				,	lowerCamelCase				:						str     )       ->							str:
        _UpperCamelCase   :				Dict       =     PoolFormerModel(config=lowerCamelCase     )
        model.to(lowerCamelCase     )
        model.eval()
        _UpperCamelCase   :				Optional[int]       =     model(lowerCamelCase     )
        _UpperCamelCase   :				str       =     self.image_size // 3_2.0
        self.parent.assertEqual(
            result.last_hidden_state.shape				,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							Dict:
        _UpperCamelCase   :				Dict       =     self.prepare_config_and_inputs()
        _UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     config_and_inputs
        _UpperCamelCase   :				str       =     {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class       __lowerCAmelCase     (				a_,		a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		Tuple 			=       (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 _lowerCamelCase  :		Any 			=       (
     {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  :		int 			=       False
 _lowerCamelCase  :		str 			=       False
 _lowerCamelCase  :		str 			=       False
 _lowerCamelCase  :		Union[str, Any] 			=       False
 _lowerCamelCase  :		List[str] 			=       False

 def     __snake_case    (	self				:						Dict     )       ->							Optional[int]:
        _UpperCamelCase   :				int       =     PoolFormerModelTester(self     )
        _UpperCamelCase   :				List[Any]       =     PoolFormerConfigTester(self				,	config_class=lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[Any]     )       ->							int:
        self.config_tester.run_common_tests()

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							int:
        _UpperCamelCase   :				Optional[Any]       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase     )

 @unittest.skip('PoolFormer does not use inputs_embeds'     )
 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        pass

 @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'     )
 def     __snake_case    (	self				:						List[Any]     )       ->							List[Any]:
        pass

 def     __snake_case    (	self				:						str     )       ->							List[str]:
        def check_hidden_states_output(lowerCamelCase				:						List[Any]				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						List[str]     ):
               _UpperCamelCase   :				Optional[int]       =     model_class(lowerCamelCase     )
               model.to(lowerCamelCase     )
               model.eval()

               with torch.no_grad():
                      _UpperCamelCase   :				Tuple       =     model(**self._prepare_for_class(lowerCamelCase				,	lowerCamelCase     )     )

               _UpperCamelCase   :				Tuple       =     outputs.hidden_states

               _UpperCamelCase   :				Optional[int]       =     self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowerCamelCase     )				,	lowerCamelCase     )

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]     )				,	[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]				,	)

        _UpperCamelCase  ,					_UpperCamelCase   :				Optional[Any]       =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCamelCase   :				Dict       =     True
               check_hidden_states_output(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCamelCase   :				Any       =     True

               check_hidden_states_output(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[Any]     )       ->							Any:
        if not self.model_tester.is_training:
               return

        _UpperCamelCase  ,					_UpperCamelCase   :				Union[str, Any]       =     self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase   :				Tuple       =     True

        for model_class in self.all_model_classes:
               if model_class in get_values(lowerCamelCase     ):
                      continue
               _UpperCamelCase   :				int       =     model_class(lowerCamelCase     )
               model.to(lowerCamelCase     )
               model.train()
               _UpperCamelCase   :				List[Any]       =     self._prepare_for_class(lowerCamelCase				,	lowerCamelCase				,	return_labels=lowerCamelCase     )
               _UpperCamelCase   :				List[Any]       =     model(**lowerCamelCase     ).loss
               loss.backward()

 def     __snake_case    (	self				:						List[str]     )       ->							List[str]:
        _UpperCamelCase  ,					_UpperCamelCase   :				Optional[Any]       =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCamelCase   :				int       =     model_class(lowerCamelCase     )
               _UpperCamelCase   :				Tuple       =     inspect.signature(model.forward     )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCamelCase   :				List[Any]       =     [*signature.parameters.keys()]

               _UpperCamelCase   :				Any       =     ['pixel_values']
               self.assertListEqual(arg_names[:1]				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						List[str]     )       ->							str:
        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCamelCase   :				Any       =     PoolFormerModel.from_pretrained(lowerCamelCase     )
               self.assertIsNotNone(lowerCamelCase     )







def SCREAMING_SNAKE_CASE (   ):
       _UpperCamelCase   :				List[str]       =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
       return image





@require_torch
class       __lowerCAmelCase     (				unittest.TestCase  ):



 '''simple docstring'''

 @slow
 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				int       =     PoolFormerImageProcessor()
        _UpperCamelCase   :				Union[str, Any]       =     PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'     ).to(lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     image_processor(images=prepare_img()				,	return_tensors='pt'     ).to(lowerCamelCase     )

        # forward pass
        with torch.no_grad():
               _UpperCamelCase   :				str       =     model(**lowerCamelCase     )

        # verify the logits
        _UpperCamelCase   :				str       =     torch.Size((1, 1_0_0_0)     )
        self.assertEqual(outputs.logits.shape				,	lowerCamelCase     )

        _UpperCamelCase   :				Union[str, Any]       =     torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]     ).to(lowerCamelCase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]				,	lowerCamelCase				,	atol=1e-4     )     )




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
UpperCAmelCase							= '''stas/tiny-wmt19-en-ru'''




class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str 			=       FSMTTokenizer
 _lowerCamelCase  :		Tuple 			=       False

 def     __snake_case    (	self				:						Dict     )       ->							Union[str, Any]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _UpperCamelCase   :				Dict       =     [
            '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   :				List[str]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[str]       =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        _UpperCamelCase   :				List[str]       =     ['en', 'ru']
        _UpperCamelCase   :				int       =     {
            'langs': self.langs,
            'src_vocab_size': 1_0,
            'tgt_vocab_size': 2_0,
        }

        _UpperCamelCase   :				Tuple       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['src_vocab_file']     )
        _UpperCamelCase   :				Optional[int]       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['tgt_vocab_file']     )
        _UpperCamelCase   :				Any       =     os.path.join(self.tmpdirname				,	'tokenizer_config.json'     )
        _UpperCamelCase   :				int       =     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     __snake_case    (	self				:						str     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )

 @cached_property
 def     __snake_case    (	self				:						Optional[int]     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Optional[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     __snake_case    (	self				:						List[str]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     FSMTTokenizer(self.langs				,	self.src_vocab_file				,	self.tgt_vocab_file				,	self.merges_file     )

        _UpperCamelCase   :				Optional[int]       =     'lower'
        _UpperCamelCase   :				str       =     ['low', 'er</w>']
        _UpperCamelCase   :				Optional[Any]       =     tokenizer.tokenize(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     tokens + ['<unk>']
        _UpperCamelCase   :				int       =     [1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     )				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     tokenizer.encode('sequence builders'				,	add_special_tokens=lowerCamelCase     )
        _UpperCamelCase   :				Any       =     tokenizer.encode('multi-sequence build'				,	add_special_tokens=lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase				,	lowerCamelCase     )

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

 @slow
 def     __snake_case    (	self				:						List[str]     )       ->							Optional[int]:
        _UpperCamelCase   :				Optional[Any]       =     self.tokenizer_en_ru
        _UpperCamelCase   :				Union[str, Any]       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     [
            [
                '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   :				Union[str, Any]       =     tokenizer_enc.encode(lowerCamelCase				,	return_tensors=lowerCamelCase     )
               self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

               # and decode backward, using the reversed languages model
               _UpperCamelCase   :				Optional[int]       =     tokenizer_dec.decode(lowerCamelCase				,	skip_special_tokens=lowerCamelCase     )
               self.assertEqual(lowerCamelCase				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,	do_lower_case=lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.tokenize('USA is United States of America'     )
        _UpperCamelCase   :				Optional[int]       =     ['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     __snake_case    (	self				:						Dict     )       ->							List[str]:
        pass

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



