
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        lowercase	:




							def __init__(       self     ,A__     ,A__=3     ,A__=3_2     ,A__=3     ,A__=1_0     ,A__=[1_0, 2_0, 3_0, 4_0]     ,A__=[1, 1, 2, 1]     ,A__=True     ,A__=True     ,A__="relu"     ,A__=3     ,A__=None     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		image_size
												lowercase					=		num_channels
												lowercase					=		embeddings_size
												lowercase					=		hidden_sizes
												lowercase					=		depths
												lowercase					=		is_training
												lowercase					=		use_labels
												lowercase					=		hidden_act
												lowercase					=		num_labels
												lowercase					=		scope
												lowercase					=		len(A__)




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

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

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




							def 				A__   (       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 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		TFRegNetModel(config=A__)
												lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		self.num_labels
												lowercase					=		TFRegNetForImageClassification(A__)
												lowercase					=		model(A__     ,labels=A__     ,training=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))





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







@require_tf
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Any       =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
							lowercase_ : Optional[int]       =(
							    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowercase_ : Any       =False
							lowercase_ : Optional[Any]       =False
							lowercase_ : Union[str, Any]       =False
							lowercase_ : Tuple       =False
							lowercase_ : Any       =False




							def 				A__   (       self):
												lowercase					=		TFRegNetModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,has_text_modality=A__)




							def 				A__   (       self):
												return




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




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




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

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

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




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 				A__   (       self):
												def check_hidden_states_output(A__     ,A__     ,A__):
																	lowercase					=		model_class(A__)
																	lowercase					=		model(**self._prepare_for_class(A__     ,A__)     ,training=A__)

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

																	lowercase					=		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]     ,)

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

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

																						check_hidden_states_output(A__     ,A__     ,A__)




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(A__     ,A__     ,A__     ,A__={}):
																	lowercase					=		model(A__     ,return_dict=A__     ,**A__)
																	lowercase					=		model(A__     ,return_dict=A__     ,**A__).to_tuple()

																	def recursive_check(A__     ,A__):
																						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:
																	lowercase					=		model_class(A__)

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

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

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

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




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A__)





							@slow
							def 				A__   (       self):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		TFRegNetModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image







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




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





							@slow
							def 				A__   (       self):
												lowercase					=		TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])

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

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

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

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

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






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    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'''				,    )

					lowercase					=		state_dict['''decoder.embed_tokens.weight''']
					lowercase					=		MaMaaaForConditionalGeneration(lowerCAmelCase__	)
					model.model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)
					lowercase					=		make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					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.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




