





'''simple docstring'''
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      snake_case     :




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

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






 def        _lowerCamelCase					(       self       :							Any	):
        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        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Optional[Any]							,	__A       :							Optional[int]							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  TFRegNetModel(config=__A	)
        __UpperCamelCase       =  model(__A							,	training=__A	)
        # 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 // 3_2, self.image_size // 3_2)							,	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Optional[int]							,	__A       :							List[str]							,	__A       :							Any	):
        __UpperCamelCase       =  self.num_labels
        __UpperCamelCase       =  TFRegNetForImageClassification(__A	)
        __UpperCamelCase       =  model(__A							,	labels=__A							,	training=__A	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.prepare_config_and_inputs()
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  config_and_inputs
        __UpperCamelCase       =  {'pixel_values': pixel_values}
        return config, inputs_dict






@require_tf
class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
 SCREAMING_SNAKE_CASE_	:					Tuple					=(
     {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_	:					List[str]					=False
 SCREAMING_SNAKE_CASE_	:					List[Any]					=False
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=False
 SCREAMING_SNAKE_CASE_	:					Tuple					=False
 SCREAMING_SNAKE_CASE_	:					int					=False






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  TFRegNetModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=__A							,	has_text_modality=__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        return






 @unittest.skip(reason='RegNet does not use inputs_embeds'	)
 def        _lowerCamelCase					(       self       :							Tuple	):
        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        _lowerCamelCase					(       self       :							Optional[Any]	):
        super().test_keras_fit()






 @unittest.skip(reason='RegNet does not support input and output embeddings'	)
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        pass






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCamelCase       =  ['pixel_values']
               self.assertListEqual(arg_names[:1]							,	__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        def check_hidden_states_output(__A       :							Any							,	__A       :							Optional[int]							,	__A       :							Union[str, Any]	):
               __UpperCamelCase       =  model_class(__A	)
               __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)							,	training=__A	)

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

               __UpperCamelCase       =  self.model_tester.num_stages
               self.assertEqual(len(__A	)							,	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]							,	)

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

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

                      check_hidden_states_output(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(__A       :							int							,	__A       :							Any							,	__A       :							List[Any]							,	__A       :							List[str]={}	):
               __UpperCamelCase       =  model(__A							,	return_dict=__A							,	**__A	)
               __UpperCamelCase       =  model(__A							,	return_dict=__A							,	**__A	).to_tuple()

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

                      recursive_check(__A							,	__A	)

        for model_class in self.all_model_classes:
               __UpperCamelCase       =  model_class(__A	)

               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A	)
               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A	)
               check_equivalence(__A							,	__A							,	__A	)

               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A							,	return_labels=__A	)
               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A							,	return_labels=__A	)
               check_equivalence(__A							,	__A							,	__A	)

               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A	)
               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A	)
               check_equivalence(__A							,	__A							,	__A							,	{'output_hidden_states': True}	)

               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A							,	return_labels=__A	)
               __UpperCamelCase       =  self._prepare_for_class(__A							,	__A							,	return_labels=__A	)
               check_equivalence(__A							,	__A							,	__A							,	{'output_hidden_states': True}	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A	)






 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  TFRegNetModel.from_pretrained(__A	)
               self.assertIsNotNone(__A	)






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



       """simple docstring"""






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


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




 """simple docstring"""



 @cached_property
 def        _lowerCamelCase					(       self       :							List[str]	):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
            if is_vision_available()
            else None
        )






 @slow
 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

        __UpperCamelCase       =  self.default_image_processor
        __UpperCamelCase       =  prepare_img()
        __UpperCamelCase       =  image_processor(images=__A							,	return_tensors='tf'	)

        # forward pass
        __UpperCamelCase       =  model(**__A							,	training=__A	)

        # verify the logits
        __UpperCamelCase       =  tf.TensorShape((1, 1_0_0_0)	)
        self.assertEqual(outputs.logits.shape							,	__A	)

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

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







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''',
        default='''facebook/mbart-large-cc25''',
        type=str,
        help='''Which huggingface architecture to use: mbart-large''',
    )
    parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
    parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							str =convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)
