

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
A											=     logging.get_logger()


@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  4_2
			__A						  =  field(default_factory=__snake_case							)
			__A						  =  field(default_factory=__snake_case							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(list(m.modules()							)							) == 1 or isinstance(UpperCAmelCase__ ,				nn.Convad							) or isinstance(UpperCAmelCase__ ,				nn.BatchNormad							)
				if has_not_submodules:
					self.traced.append(UpperCAmelCase__							)


			def __call__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				for m in self.module.modules():
					self.handles.append(m.register_forward_hook(self._forward_hook							)							)
				self.module(UpperCAmelCase__							)
				[x.remove() for x in self.handles]
				return self







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return list(filter(lambda __UpperCamelCase							: len(list(x.state_dict().keys()							)							) > 0 ,				self.traced							)							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  4_2
			__A						  =  4_2
			__A						  =  1
			__A						  =  field(default_factory=__snake_case							)
			__A						  =  field(default_factory=__snake_case							)
			__A						  =  True


			def __call__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= Tracker(self.dest							)(UpperCAmelCase__							).parametrized
				snake_case_      		= Tracker(self.src							)(UpperCAmelCase__							).parametrized

				snake_case_      		= list(filter(lambda __UpperCamelCase							: type(UpperCAmelCase__							) not in self.src_skip ,				UpperCAmelCase__							)							)
				snake_case_      		= list(filter(lambda __UpperCamelCase							: type(UpperCAmelCase__							) not in self.dest_skip ,				UpperCAmelCase__							)							)

				if len(UpperCAmelCase__							) != len(UpperCAmelCase__							) and self.raise_if_mismatch:
					raise Exception(
					    f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__							)} operations while"""
					    f""" destination module has {len(UpperCAmelCase__							)}."""							)

				for dest_m, src_m in zip(UpperCAmelCase__ ,				UpperCAmelCase__							):
					dest_m.load_state_dict(src_m.state_dict()							)
					if self.verbose == 1:
						print(f"""Transfered from={src_m} to={dest_m}"""							)







class SCREAMING_SNAKE_CASE  (		nn.Module							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= []
				# - get the stem
				feature_blocks.append(('conv1', model.stem)							)
				# - get all the feature blocks
				for k, v in model.trunk_output.named_children():
					assert k.startswith('block'							), f"""Unexpected layer name {k}"""
					snake_case_      		= len(UpperCAmelCase__							) + 1
					feature_blocks.append((f"""res{block_index}""", v)							)

				snake_case_      		= nn.ModuleDict(UpperCAmelCase__							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return get_trunk_forward_outputs(
				    UpperCAmelCase__ ,				out_feat_keys=UpperCAmelCase__ ,				feature_blocks=self._feature_blocks ,				)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= x.split('-'							)
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)







			def __getitem__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				if x not in self:
					snake_case_      		= self.convert_name_to_timm(UpperCAmelCase__							)
					snake_case_      		= partial(lambda: (timm.create_model(UpperCAmelCase__ ,				pretrained=UpperCAmelCase__							).eval(), None)							)

				else:
					snake_case_      		= super().__getitem__(UpperCAmelCase__							)

				return val







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __getitem__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				if "seer" in x and "in1k" not in x:
					snake_case_      		= RegNetModel
				else:
					snake_case_      		= RegNetForImageClassification
				return val







def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''


	for from_key, to_key in keys:
		snake_case_      		= from_state_dict[from_key].clone()
		print(f"""Copied key={from_key} to={to_key}"""	)
	return to_state_dict





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = True			, ):





	'''simple docstring'''
	print(f"""Converting {name}..."""	)
	with torch.no_grad():
		snake_case_		,       snake_case_      		= from_model_func()
		snake_case_      		= our_model_func(_UpperCamelCase	).eval()
		snake_case_      		= ModuleTransfer(src=_UpperCamelCase			, dest=_UpperCamelCase			, raise_if_mismatch=_UpperCamelCase	)
		snake_case_      		= torch.randn((1, 3, 224, 224)	)
		module_transfer(_UpperCamelCase	)

	if from_state_dict is not None:
		snake_case_      		= []
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			snake_case_      		= [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
		snake_case_      		= manually_copy_vissl_head(_UpperCamelCase			, our_model.state_dict()			, _UpperCamelCase	)
		our_model.load_state_dict(_UpperCamelCase	)

	snake_case_      		= our_model(_UpperCamelCase			, output_hidden_states=_UpperCamelCase	)
	snake_case_      		= (
	    our_outputs.logits if isinstance(_UpperCamelCase			, _UpperCamelCase	) else our_outputs.last_hidden_state
	)

	snake_case_      		= from_model(_UpperCamelCase	)
	snake_case_      		= from_output[-1] if type(_UpperCamelCase	) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		snake_case_      		= our_outputs.hidden_states[-1]

	assert torch.allclose(_UpperCamelCase			, _UpperCamelCase	), "The model logits don't match the original one."

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / name			, commit_message='Add model'			, use_temp_dir=_UpperCamelCase			, )

		snake_case_      		= 224 if 'seer' not in name else 384
		# we can use the convnext one
		snake_case_      		= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'			, size=_UpperCamelCase	)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name			, commit_message='Add image processor'			, use_temp_dir=_UpperCamelCase			, )

		print(f"""Pushed {name}"""	)





def a(lowercase__			, lowercase__ = None			, lowercase__ = True	):





	'''simple docstring'''
	snake_case_      		= 'imagenet-1k-id2label.json'
	snake_case_      		= 1000
	snake_case_      		= (1, num_labels)

	snake_case_      		= 'huggingface/label-files'
	snake_case_      		= num_labels
	snake_case_      		= json.load(open(cached_download(hf_hub_url(_UpperCamelCase			, _UpperCamelCase			, repo_type='dataset'	)	)			, 'r'	)	)
	snake_case_      		= {int(_UpperCamelCase	): v for k, v in idalabel.items()}

	snake_case_      		= idalabel
	snake_case_      		= {v: k for k, v in idalabel.items()}

	snake_case_      		= partial(_UpperCamelCase			, num_labels=_UpperCamelCase			, idalabel=_UpperCamelCase			, labelaid=_UpperCamelCase	)

	snake_case_      		= {
	    'regnet-x-002': ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]			, hidden_sizes=[24, 56, 152, 368]			, groups_width=8			, layer_type='x'	),
	    'regnet-x-004': ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]			, hidden_sizes=[32, 64, 160, 384]			, groups_width=16			, layer_type='x'	),
	    'regnet-x-006': ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]			, hidden_sizes=[48, 96, 240, 528]			, groups_width=24			, layer_type='x'	),
	    'regnet-x-008': ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]			, hidden_sizes=[64, 128, 288, 672]			, groups_width=16			, layer_type='x'	),
	    'regnet-x-016': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]			, hidden_sizes=[72, 168, 408, 912]			, groups_width=24			, layer_type='x'	),
	    'regnet-x-032': ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]			, hidden_sizes=[96, 192, 432, 1008]			, groups_width=48			, layer_type='x'	),
	    'regnet-x-040': ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]			, hidden_sizes=[80, 240, 560, 1360]			, groups_width=40			, layer_type='x'	),
	    'regnet-x-064': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]			, hidden_sizes=[168, 392, 784, 1624]			, groups_width=56			, layer_type='x'	),
	    'regnet-x-080': ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]			, hidden_sizes=[80, 240, 720, 1920]			, groups_width=120			, layer_type='x'	),
	    'regnet-x-120': ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]			, hidden_sizes=[224, 448, 896, 2240]			, groups_width=112			, layer_type='x'	),
	    'regnet-x-160': ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]			, hidden_sizes=[256, 512, 896, 2048]			, groups_width=128			, layer_type='x'	),
	    'regnet-x-320': ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]			, hidden_sizes=[336, 672, 1344, 2520]			, groups_width=168			, layer_type='x'	),
	    # y variant
	    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			, hidden_sizes=[24, 56, 152, 368]			, groups_width=8	),
	    'regnet-y-004': ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]			, hidden_sizes=[48, 104, 208, 440]			, groups_width=8	),
	    'regnet-y-006': ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]			, hidden_sizes=[48, 112, 256, 608]			, groups_width=16	),
	    'regnet-y-008': ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]			, hidden_sizes=[64, 128, 320, 768]			, groups_width=16	),
	    'regnet-y-016': ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]			, hidden_sizes=[48, 120, 336, 888]			, groups_width=24	),
	    'regnet-y-032': ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]			, hidden_sizes=[72, 216, 576, 1512]			, groups_width=24	),
	    'regnet-y-040': ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]			, hidden_sizes=[128, 192, 512, 1088]			, groups_width=64	),
	    'regnet-y-064': ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]			, hidden_sizes=[144, 288, 576, 1296]			, groups_width=72	),
	    'regnet-y-080': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]			, hidden_sizes=[168, 448, 896, 2016]			, groups_width=56	),
	    'regnet-y-120': ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]			, hidden_sizes=[224, 448, 896, 2240]			, groups_width=112	),
	    'regnet-y-160': ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]			, hidden_sizes=[224, 448, 1232, 3024]			, groups_width=112	),
	    'regnet-y-320': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]			, hidden_sizes=[232, 696, 1392, 3712]			, groups_width=232	),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]			, hidden_sizes=[232, 696, 1392, 3712]			, groups_width=232	),
	    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]			, hidden_sizes=[328, 984, 1968, 4920]			, groups_width=328	),
	    'regnet-y-1280-seer': RegNetConfig(
	        depths=[2, 7, 17, 1]			, hidden_sizes=[528, 1056, 2904, 7392]			, groups_width=264	),
	    'regnet-y-2560-seer': RegNetConfig(
	        depths=[3, 7, 16, 1]			, hidden_sizes=[640, 1696, 2544, 5088]			, groups_width=640	),
	    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]			, hidden_sizes=[2020, 4040, 11110, 28280]			, groups_width=1010	),
	    # finetuned on imagenet
	    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]			, hidden_sizes=[232, 696, 1392, 3712]			, groups_width=232	),
	    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]			, hidden_sizes=[328, 984, 1968, 4920]			, groups_width=328	),
	    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]			, hidden_sizes=[528, 1056, 2904, 7392]			, groups_width=264	),
	    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]			, hidden_sizes=[640, 1696, 2544, 5088]			, groups_width=640	),
	    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]			, hidden_sizes=[2020, 4040, 11110, 28280]			, groups_width=1010	),
	}

	snake_case_      		= NameToOurModelFuncMap()
	snake_case_      		= NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(lowercase__			, lowercase__	) -> Tuple[nn.Module, Dict]:
		snake_case_      		= torch.hub.load_state_dict_from_url(_UpperCamelCase			, model_dir=str(_UpperCamelCase	)			, map_location='cpu'	)
		snake_case_      		= model_func()
		# check if we have a head, if yes add it
		snake_case_      		= files['classy_state_dict']['base_model']['model']
		snake_case_      		= model_state_dict['trunk']
		model.load_state_dict(_UpperCamelCase	)
		return model.eval(), model_state_dict["heads"]

	# pretrained
	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'			, lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27			, group_width=1010			, w_a=1744			, w_a=620.83			, w_m=2.52	)	)	)			, )

	# IN1K finetuned
	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'			, lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)			, )

	snake_case_      		= partial(
	    _UpperCamelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'			, lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27			, group_width=1010			, w_a=1744			, w_a=620.83			, w_m=2.52	)	)	)			, )

	if model_name:
		convert_weight_and_push(
		    _UpperCamelCase			, names_to_from_model_map[model_name]			, names_to_ours_model_map[model_name]			, names_to_config[model_name]			, _UpperCamelCase			, _UpperCamelCase			, )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    _UpperCamelCase			, names_to_from_model_map[model_name]			, names_to_ours_model_map[model_name]			, _UpperCamelCase			, _UpperCamelCase			, _UpperCamelCase			, )
	return config, expected_shape


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default=None,
		    type=str,
		    help=(
		        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
		        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=Path,
		    required=True,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    default=True,
		    type=bool,
		    required=False,
		    help='If True, push model and image processor to the hub.',
		)

		A											=     parser.parse_args()

		A											=     args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import (
		    MraForMaskedLM,
		    MraForMultipleChoice,
		    MraForQuestionAnswering,
		    MraForSequenceClassification,
		    MraForTokenClassification,
		    MraModel,
		)
		from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=2 ,				__UpperCamelCase=8 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=99 ,				__UpperCamelCase=16 ,				__UpperCamelCase=5 ,				__UpperCamelCase=2 ,				__UpperCamelCase=36 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=16 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=3 ,				__UpperCamelCase=4 ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= seq_length
				snake_case_      		= is_training
				snake_case_      		= use_input_mask
				snake_case_      		= use_token_type_ids
				snake_case_      		= use_labels
				snake_case_      		= vocab_size
				snake_case_      		= hidden_size
				snake_case_      		= num_hidden_layers
				snake_case_      		= num_attention_heads
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= max_position_embeddings
				snake_case_      		= type_vocab_size
				snake_case_      		= type_sequence_label_size
				snake_case_      		= initializer_range
				snake_case_      		= num_labels
				snake_case_      		= num_choices
				snake_case_      		= scope


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

				snake_case_      		= None
				if self.use_input_mask:
					snake_case_      		= random_attention_mask([self.batch_size, self.seq_length]							)

				snake_case_      		= None
				if self.use_token_type_ids:
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size							)

				snake_case_      		= None
				snake_case_      		= None
				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_choices							)

				snake_case_      		= self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MraConfig(
				    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				is_decoder=__UpperCamelCase ,				initializer_range=self.initializer_range ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_config()
				snake_case_      		= 3_00
				return config


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				(
				    (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       
				)      		= self.prepare_config_and_inputs()

				snake_case_      		= True
				snake_case_      		= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
				snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2							)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= True
				snake_case_      		= MraModel(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				)
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				)
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraForQuestionAnswering(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				start_positions=__UpperCamelCase ,				end_positions=__UpperCamelCase ,				)
				self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)							)
				self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MraForSequenceClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MraForTokenClassification(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_choices
				snake_case_      		= MraForMultipleChoice(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= input_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= token_type_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= input_mask.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase ,				)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				(
				    (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       
				)      		= config_and_inputs
				snake_case_      		= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False

			__A						  =  ()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					snake_case_      		= type
					self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= MraModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







			@unittest.skip(reason='MRA does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return







@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraModel.from_pretrained('uw-madison/mra-base-512-4'							)
				snake_case_      		= torch.arange(2_56							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= torch.Size((1, 2_56, 7_68)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'							)
				snake_case_      		= torch.arange(2_56							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= 5_02_65

				snake_case_      		= torch.Size((1, 2_56, vocab_size)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'							)
				snake_case_      		= torch.arange(40_96							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= 5_02_65

				snake_case_      		= torch.Size((1, 40_96, vocab_size)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)
