






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor
class     lowerCamelCase_					:







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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								__magic_name__		:Optional[int]   =   None
								if self.use_labels:
												__magic_name__		:Union[str, Any]   =   ids_tensor([self.batch_size]							,       self.num_labels  )

								__magic_name__		:List[Any]   =   self.get_config()

								return config, pixel_values, labels







				def       A						(    self  ):
								"""simple docstring"""
								return ResNetConfig(
								    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							,       image_size=self.image_size							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   TFResNetModel(config=__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   model(__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 // 3_2, self.image_size // 3_2)							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.num_labels
								__magic_name__		:Optional[int]   =   TFResNetForImageClassification(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   model(__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.prepare_config_and_inputs()
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Optional[int]   =   config_and_inputs
								__magic_name__		:List[Any]   =   {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_tf
class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				a__       							=	(
				    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				a__       							=	False
				a__       							=	False
				a__       							=	False
				a__       							=	False
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   TFResNetModelTester(self  )
								__magic_name__		:List[str]   =   ConfigTester(self							,       config_class=__lowerCAmelCase							,       has_text_modality=__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()







				def       A						(    self  ):
								"""simple docstring"""
								return







				@unittest.skip(reason='''ResNet does not use inputs_embeds'''  )
				def       A						(    self  ):
								"""simple docstring"""
								pass







				@unittest.skip(reason='''ResNet does not support input and output embeddings'''  )
				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:List[Any]   =   self.model_tester.prepare_config_and_inputs_for_common()

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

												__magic_name__		:Optional[Any]   =   ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
												__magic_name__		:List[Any]   =   model_class(__lowerCAmelCase  )
												__magic_name__		:int   =   model(**self._prepare_for_class(__lowerCAmelCase							,       __lowerCAmelCase  )  )

												__magic_name__		:List[Any]   =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												__magic_name__		:Tuple   =   self.model_tester.num_stages
												self.assertEqual(len(__lowerCAmelCase  )							,       expected_num_stages + 1  )

												# ResNet'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 // 4, self.model_tester.image_size // 4]							,       )

								__magic_name__  ,					__magic_name__		:List[str]   =   self.model_tester.prepare_config_and_inputs_for_common()
								__magic_name__		:List[str]   =   ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																__magic_name__		:Optional[int]   =   layer_type
																__magic_name__		:List[Any]   =   True
																check_hidden_states_output(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

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

																check_hidden_states_output(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:Tuple   =   TFResNetModel.from_pretrained(__lowerCAmelCase  )
												self.assertIsNotNone(__lowerCAmelCase  )







def   __lowercase       (			):







				"""simple docstring"""






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

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







				@cached_property
				def       A						(    self  ):
								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
								    if is_vision_available()
								    else None
								)







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								__magic_name__		:Any   =   self.default_image_processor
								__magic_name__		:List[str]   =   prepare_img()
								__magic_name__		:str   =   image_processor(images=__lowerCAmelCase							,       return_tensors='''tf'''  )

								# forward pass
								__magic_name__		:List[Any]   =   model(**__lowerCAmelCase  )

								# verify the logits
								__magic_name__		:Tuple   =   tf.TensorShape((1, 1_0_0_0)  )
								self.assertEqual(outputs.logits.shape							,       __lowerCAmelCase  )

								__magic_name__		:Tuple   =   tf.constant([-11.1069, -9.7877, -8.3777]  )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,       __lowerCAmelCase							,       atol=1E-4  )  )














import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   emb.weight.shape
				__magic_name__		:int   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:str   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:Optional[Any]   =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__magic_name__		:List[Any]   =   mam_aaa['''model''']
				remove_ignore_keys_(snake_case			)
				__magic_name__		:Tuple   =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:List[str]   =   MaMaaaConfig(
				    vocab_size=snake_case,	max_position_embeddings=1_0_2_4,	encoder_layers=args.encoder_layers,	decoder_layers=args.decoder_layers,	encoder_attention_heads=args.encoder_attention_heads,	decoder_attention_heads=args.decoder_attention_heads,	encoder_ffn_dim=args.encoder_ffn_embed_dim,	decoder_ffn_dim=args.decoder_ffn_embed_dim,	d_model=args.encoder_embed_dim,	encoder_layerdrop=args.encoder_layerdrop,	decoder_layerdrop=args.decoder_layerdrop,	dropout=args.dropout,	attention_dropout=args.attention_dropout,	activation_dropout=args.activation_dropout,	activation_function='''relu''',	)

				__magic_name__		:int   =   state_dict['''decoder.embed_tokens.weight''']
				__magic_name__		:List[str]   =   MaMaaaForConditionalGeneration(snake_case			)
				model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:List[str]   =   make_linear_from_emb(model.model.shared			)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""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.""")
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()
					SCREAMING_SNAKE_CASE__   :      Any				=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)






