






'''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 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=3   ,_lowerCAmelCase=32   ,_lowerCAmelCase=3   ,_lowerCAmelCase=10   ,_lowerCAmelCase=[10, 20, 30, 40]   ,_lowerCAmelCase=[1, 1, 2, 1]   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase="relu"   ,_lowerCAmelCase=3   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  embeddings_size
								lowerCamelCase__         =  hidden_sizes
								lowerCamelCase__         =  depths
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_labels
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  num_labels
								lowerCamelCase__         =  scope
								lowerCamelCase__         =  len(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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






		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  TFRegNetModel(config=_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,training=_lowerCAmelCase       )
								# 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 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.num_labels
								lowerCamelCase__         =  TFRegNetForImageClassification(_lowerCAmelCase       )
								lowerCamelCase__         =  model(_lowerCAmelCase   ,labels=_lowerCAmelCase   ,training=_lowerCAmelCase       )
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  config_and_inputs
								lowerCamelCase__         =  {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCamelCase__    (a   ,a   ,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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFRegNetModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=_lowerCAmelCase   ,has_text_modality=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								return






		@unittest.skip(reason="""RegNet does not use inputs_embeds"""       )
		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self       ):
								super().test_keras_fit()






		@unittest.skip(reason="""RegNet does not support input and output embeddings"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__         =  ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								def check_hidden_states_output(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )
														lowerCamelCase__         =  model(**self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )   ,training=_lowerCAmelCase       )

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

														lowerCamelCase__         =  self.model_tester.num_stages
														self.assertEqual(len(_lowerCAmelCase       )   ,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]   ,)

								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__         =  ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				lowerCamelCase__         =  layer_type
																				lowerCamelCase__         =  True
																				check_hidden_states_output(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )

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

																				check_hidden_states_output(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase={}       ):
														lowerCamelCase__         =  model(_lowerCAmelCase   ,return_dict=_lowerCAmelCase   ,**_lowerCAmelCase       )
														lowerCamelCase__         =  model(_lowerCAmelCase   ,return_dict=_lowerCAmelCase   ,**_lowerCAmelCase       ).to_tuple()

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

																				recursive_check(_lowerCAmelCase   ,_lowerCAmelCase       )

								for model_class in self.all_model_classes:
														lowerCamelCase__         =  model_class(_lowerCAmelCase       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														check_equivalence(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase   ,return_labels=_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase   ,return_labels=_lowerCAmelCase       )
														check_equivalence(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase       )
														check_equivalence(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,{"""output_hidden_states""": True}       )

														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase   ,return_labels=_lowerCAmelCase       )
														lowerCamelCase__         =  self._prepare_for_class(_lowerCAmelCase   ,_lowerCAmelCase   ,return_labels=_lowerCAmelCase       )
														check_equivalence(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,{"""output_hidden_states""": True}       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCamelCase__         =  TFRegNetModel.from_pretrained(_lowerCAmelCase       )
														self.assertIsNotNone(_lowerCAmelCase       )



def 						A__			(				):
						lowerCamelCase__         =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image





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




		'''simple docstring'''





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






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(images=_lowerCAmelCase   ,return_tensors="""tf"""       )

								# forward pass
								lowerCamelCase__         =  model(**_lowerCAmelCase   ,training=_lowerCAmelCase       )

								# verify the logits
								lowerCamelCase__         =  tf.TensorShape((1, 10_00)       )
								self.assertEqual(outputs.logits.shape   ,_lowerCAmelCase       )

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

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









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		argparse.ArgumentParser()
							parser.add_argument('--dialogpt_path', default='.', type=str)
							UpperCamelCase    :      Dict  			=		parser.parse_args()
							for MODEL in DIALOGPT_MODELS:
														UpperCamelCase    :      Any  			=		os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
														UpperCamelCase    :      str  			=		F'./DialoGPT-{MODEL}'
														convert_dialogpt_checkpoint(
														    checkpoint_path,
														    pytorch_dump_folder_path,
														)

