




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Optional[Any]=3   ,					UpperCAmelCase__ :      Optional[Any]=32   ,					UpperCAmelCase__ :      Union[str, Any]=3   ,					UpperCAmelCase__ :      List[Any]=10   ,					UpperCAmelCase__ :      int=[10, 20, 30, 40]   ,					UpperCAmelCase__ :      str=[1, 1, 2, 1]   ,					UpperCAmelCase__ :      Optional[int]=True   ,					UpperCAmelCase__ :      Any=True   ,					UpperCAmelCase__ :      List[str]="relu"   ,					UpperCAmelCase__ :      Optional[int]=3   ,					UpperCAmelCase__ :      Tuple=None   ,					)		->List[Any]:





           '''simple docstring'''

           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       image_size
           A__ 					=       num_channels
           A__ 					=       embeddings_size
           A__ 					=       hidden_sizes
           A__ 					=       depths
           A__ 					=       is_training
           A__ 					=       use_labels
           A__ 					=       hidden_act
           A__ 					=       num_labels
           A__ 					=       scope
           A__ 					=       len(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ 					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           A__ 					=       self.get_config()
           return config, pixel_values, labels


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Any:





           '''simple docstring'''

           return RegNetConfig(
               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   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Union[str, Any])		->Tuple:





           '''simple docstring'''

           A__ 					=       TFRegNetModel(config=UpperCAmelCase__)
           A__ 					=       model(UpperCAmelCase__   ,					training=UpperCAmelCase__)
           # 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 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Dict)		->Any:





           '''simple docstring'''

           A__ 					=       self.num_labels
           A__ 					=       TFRegNetForImageClassification(UpperCAmelCase__)
           A__ 					=       model(UpperCAmelCase__   ,					labels=UpperCAmelCase__   ,					training=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels))





    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       self.prepare_config_and_inputs()
           A__					,				A__					,				A__ 					=       config_and_inputs
           A__ 					=       {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_tf
class 							UpperCamelCase_       (					UpperCAmelCase__						,   UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
    UpperCAmelCase__				    = (
        {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
        if is_tf_available()
        else {}
    )

    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Tuple:





           '''simple docstring'''

           A__ 					=       TFRegNetModelTester(self)
           A__ 					=       ConfigTester(self   ,					config_class=UpperCAmelCase__   ,					has_text_modality=UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Tuple:





           '''simple docstring'''

           return


    @unittest.skip(reason='''RegNet does not use inputs_embeds''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->int:





           '''simple docstring'''

           pass


    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('''GPU''')) == 0   ,					reason='''TF does not support backprop for grouped convolutions on CPU.'''   ,					)
    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->str:





           '''simple docstring'''

           super().test_keras_fit()


    @unittest.skip(reason='''RegNet does not support input and output embeddings''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           A__					,				A__ 					=       self.model_tester.prepare_config_and_inputs_for_common()

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

                  A__ 					=       ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Tuple:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Dict:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      Optional[Any]):
                  A__ 					=       model_class(UpperCAmelCase__)
                  A__ 					=       model(**self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__)   ,					training=UpperCAmelCase__)

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

                  A__ 					=       self.model_tester.num_stages
                  self.assertEqual(len(UpperCAmelCase__)   ,					expected_num_stages + 1)

                  # RegNet'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 // 2, self.model_tester.image_size // 2]   ,					)

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

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

                         check_hidden_states_output(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           A__					,				A__ 					=       self.model_tester.prepare_config_and_inputs_for_common()

           def check_equivalence(UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Dict={}):
                  A__ 					=       model(UpperCAmelCase__   ,					return_dict=UpperCAmelCase__   ,					**UpperCAmelCase__)
                  A__ 					=       model(UpperCAmelCase__   ,					return_dict=UpperCAmelCase__   ,					**UpperCAmelCase__).to_tuple()

                  def recursive_check(UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      List[str]):
                         if isinstance(UpperCAmelCase__   ,					(List, Tuple)):
                                for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase__   ,					UpperCAmelCase__):
                                       recursive_check(UpperCAmelCase__   ,					UpperCAmelCase__)
                         elif tuple_object is None:
                                return
                         else:
                                self.assertTrue(
                                    all(tf.equal(UpperCAmelCase__   ,					UpperCAmelCase__))   ,					msg=(
                                        '''Tuple and dict output are not equal. Difference:'''
                                        f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
                                    )   ,					)

                         recursive_check(UpperCAmelCase__   ,					UpperCAmelCase__)

           for model_class in self.all_model_classes:
                  A__ 					=       model_class(UpperCAmelCase__)

                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__)
                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__)
                  check_equivalence(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)

                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__   ,					return_labels=UpperCAmelCase__)
                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__   ,					return_labels=UpperCAmelCase__)
                  check_equivalence(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)

                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__)
                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__)
                  check_equivalence(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					{'''output_hidden_states''': True})

                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__   ,					return_labels=UpperCAmelCase__)
                  A__ 					=       self._prepare_for_class(UpperCAmelCase__   ,					UpperCAmelCase__   ,					return_labels=UpperCAmelCase__)
                  check_equivalence(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					{'''output_hidden_states''': True})


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





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

           for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__ 					=       TFRegNetModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







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



       """simple docstring"""

       A__ 					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


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

    '''simple docstring'''






    @cached_property
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->List[Any]:





           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->int:





           '''simple docstring'''

           A__ 					=       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])

           A__ 					=       self.default_image_processor
           A__ 					=       prepare_img()
           A__ 					=       image_processor(images=UpperCAmelCase__   ,					return_tensors='''tf''')

           # forward pass
           A__ 					=       model(**UpperCAmelCase__   ,					training=UpperCAmelCase__)

           # verify the logits
           A__ 					=       tf.TensorShape((1, 1_000))
           self.assertEqual(outputs.logits.shape   ,					UpperCAmelCase__)

           A__ 					=       tf.constant([-0.4180, -1.5051, -3.4836])

           tf.debugging.assert_near(outputs.logits[0, :3]   ,					UpperCAmelCase__   ,					atol=1e-4)









"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




