
'''simple docstring'''

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				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    self.config_class(**self.inputs_dict	)
          self.parent.assertTrue(hasattr(lowerCAmelCase__						,	"""hidden_sizes"""	)	)
          self.parent.assertTrue(hasattr(lowerCAmelCase__						,	"""num_encoder_blocks"""	)	)







class 	__lowerCAmelCase    :



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



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

          _UpperCAmelCase					:  Dict         =    None
          if self.use_labels:
               _UpperCAmelCase					:  List[str]         =    ids_tensor([self.batch_size, self.image_size, self.image_size]						,	self.num_labels	)

          _UpperCAmelCase					:  Dict         =    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						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    PoolFormerModel(config=lowerCAmelCase__	)
          model.to(lowerCAmelCase__	)
          model.eval()
          _UpperCAmelCase					:  str         =    model(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    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	):
          _UpperCAmelCase					:  Optional[int]         =    self.prepare_config_and_inputs()
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  str         =    config_and_inputs
          _UpperCAmelCase					:  List[Any]         =    {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class 	__lowerCAmelCase    (  __a       ,					__a       ,					unittest.TestCase				):
     snake_case   :    Tuple							      =      (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     snake_case   :    Tuple							      =      (
         {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     snake_case   :    Union[str, Any]							      =      False
     snake_case   :    List[Any]							      =      False
     snake_case   :    str							      =      False
     snake_case   :    Tuple							      =      False
     snake_case   :    Any							      =      False



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    PoolFormerModelTester(self	)
          _UpperCAmelCase					:  Optional[int]         =    PoolFormerConfigTester(self						,	config_class=lowerCAmelCase__	)



     def     snake_case_ (self	):
          self.config_tester.run_common_tests()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    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	):
          pass



     @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""	)
     def     snake_case_ (self	):
          pass



     def     snake_case_ (self	):
          def check_hidden_states_output(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
               _UpperCAmelCase					:  Dict         =    model_class(lowerCAmelCase__	)
               model.to(lowerCAmelCase__	)
               model.eval()

               with torch.no_grad():
                    _UpperCAmelCase					:  Any         =    model(**self._prepare_for_class(lowerCAmelCase__						,	lowerCAmelCase__	)	)

               _UpperCAmelCase					:  List[str]         =    outputs.hidden_states

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

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  Any         =    True
               check_hidden_states_output(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)

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

               check_hidden_states_output(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self	):
          if not self.model_tester.is_training:
               return

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

          for model_class in self.all_model_classes:
               if model_class in get_values(lowerCAmelCase__	):
                    continue
               _UpperCAmelCase					:  Union[str, Any]         =    model_class(lowerCAmelCase__	)
               model.to(lowerCAmelCase__	)
               model.train()
               _UpperCAmelCase					:  Optional[Any]         =    self._prepare_for_class(lowerCAmelCase__						,	lowerCAmelCase__						,	return_labels=lowerCAmelCase__	)
               _UpperCAmelCase					:  List[Any]         =    model(**lowerCAmelCase__	).loss
               loss.backward()



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  str         =    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  Dict         =    model_class(lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase					:  Any         =    [*signature.parameters.keys()]

               _UpperCAmelCase					:  Any         =    ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]						,	lowerCAmelCase__	)



     @slow
     def     snake_case_ (self	):
          for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase					:  List[str]         =    PoolFormerModel.from_pretrained(lowerCAmelCase__	)
               self.assertIsNotNone(lowerCAmelCase__	)







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







@require_torch
class 	__lowerCAmelCase    (  unittest.TestCase				):



     @slow
     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    PoolFormerImageProcessor()
          _UpperCAmelCase					:  Any         =    PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    image_processor(images=prepare_img()						,	return_tensors="""pt"""	).to(lowerCAmelCase__	)

          # forward pass
          with torch.no_grad():
               _UpperCAmelCase					:  Optional[Any]         =    model(**lowerCAmelCase__	)

          # verify the logits
          _UpperCAmelCase					:  int         =    torch.Size((1, 1_0_0_0)	)
          self.assertEqual(outputs.logits.shape						,	lowerCAmelCase__	)

          _UpperCAmelCase					:  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	)	)




'''simple docstring'''

import io
import json
import subprocess


lowerCAmelCase_			:			Tuple			   =     [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

lowerCAmelCase_			:			str			   =     8







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Union[str, Any]         =    {}
     for src, tgt in pairs:
          _UpperCAmelCase					:  Union[str, Any]         =    f"{src}-{tgt}"
          _UpperCAmelCase					:  List[Any]         =    f"sacrebleu -t wmt19 -l {pair} --echo src".split()
          _UpperCAmelCase					:  List[str]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  str         =    f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
          _UpperCAmelCase					:  List[Any]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  int         =    {"""src""": src_lines[:n_objs], """tgt""": tgt_lines[:n_objs]}
     return text


lowerCAmelCase_			:			Optional[int]			   =     get_all_data(pairs, n_objs)
lowerCAmelCase_			:			str			   =     '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   lowerCAmelCase_			:			Tuple			   =     json.dump(text, f, indent=2, ensure_ascii=False)


