


'''simple docstring'''
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()
lowerCAmelCase   : str             =				logging.get_logger()




@dataclass
class        _UpperCamelCase	:


				'''simple docstring'''


				_snake_case						       =				42
				_snake_case						       =				field(default_factory=SCREAMING_SNAKE_CASE)
				_snake_case						       =				field(default_factory=SCREAMING_SNAKE_CASE)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Tuple:
					lowercase						:		Optional[Any]   					=		len(list(m.modules()							)							) == 1 or isinstance(a_							,      nn.Convad							) or isinstance(a_							,      nn.BatchNormad							)
					if has_not_submodules:
						self.traced.append(a_							)

				def __call__(     self							,      a_							)    ->  Dict:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook							)							)
					self.module(a_							)
					[x.remove() for x in self.handles]
					return self


				@property
				def 	a__		(     self							)    ->  Any:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda a_							: len(list(x.state_dict().keys()							)							) > 0							,      self.traced							)							)




@dataclass
class        _UpperCamelCase	:


				'''simple docstring'''


				_snake_case						       =				42
				_snake_case						       =				42
				_snake_case						       =				1
				_snake_case						       =				field(default_factory=SCREAMING_SNAKE_CASE)
				_snake_case						       =				field(default_factory=SCREAMING_SNAKE_CASE)
				_snake_case						       =				True

				def __call__(     self							,      a_							)    ->  Tuple:
					lowercase						:		Optional[int]   					=		Tracker(self.dest							)(a_							).parametrized
					lowercase						:		Tuple   					=		Tracker(self.src							)(a_							).parametrized

					lowercase						:		Optional[int]   					=		list(filter(lambda a_							: type(a_							) not in self.src_skip							,      a_							)							)
					lowercase						:		str   					=		list(filter(lambda a_							: type(a_							) not in self.dest_skip							,      a_							)							)

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

					for dest_m, src_m in zip(a_							,      a_							):
						dest_m.load_state_dict(src_m.state_dict()							)
						if self.verbose == 1:
							print(F'''Transfered from={src_m} to={dest_m}'''							)




class        _UpperCamelCase	(    nn.Module):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Union[str, Any]:
					super().__init__()

					lowercase						:		List[Tuple[str, nn.Module]]   					=		[]
					# - 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}'''
						lowercase						:		Union[str, Any]   					=		len(a_							) + 1
						feature_blocks.append((F'''res{block_index}''', v)							)

					lowercase						:		Any   					=		nn.ModuleDict(a_							)


				def 	a__		(     self							,      a_							)    ->  List[Any]:
					return get_trunk_forward_outputs(
					    a_							,      out_feat_keys=a_							,      feature_blocks=self._feature_blocks							,      )




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							,      a_							)    ->  str:
					lowercase						:		Tuple   					=		x.split("-"							)
					return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)


				def __getitem__(     self							,      a_							)    ->  Callable[[], Tuple[nn.Module, Dict]]:
					# default to timm!
					if x not in self:
						lowercase						:		Any   					=		self.convert_name_to_timm(a_							)
						lowercase						:		Union[str, Any]   					=		partial(lambda: (timm.create_model(a_							,      pretrained=a_							).eval(), None)							)

					else:
						lowercase						:		Union[str, Any]   					=		super().__getitem__(a_							)

					return val




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __getitem__(     self							,      a_							)    ->  Callable[[], nn.Module]:
					if "seer" in x and "in1k" not in x:
						lowercase						:		Union[str, Any]   					=		RegNetModel
					else:
						lowercase						:		List[str]   					=		RegNetForImageClassification
					return val




def 	_A  (       A							,A							,A							)  ->				Union[str, Any]:
	for from_key, to_key in keys:
		lowercase						:		int   					=		from_state_dict[from_key].clone()
		print(F'''Copied key={from_key} to={to_key}'''							)
	return to_state_dict




def 	_A  (       A							,A							,A							,A							,A							,A = True							,)  ->				Optional[Any]:
	print(F'''Converting {name}...'''							)
	with torch.no_grad():
		lowercase					,		lowercase						:		Optional[int]   					=		from_model_func()
		lowercase						:		List[Any]   					=		our_model_func(A							).eval()
		lowercase						:		List[str]   					=		ModuleTransfer(src=A							,dest=A							,raise_if_mismatch=A							)
		lowercase						:		Optional[int]   					=		torch.randn((1, 3, 2_2_4, 2_2_4)							)
		module_transfer(A							)

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

	lowercase						:		Optional[Any]   					=		our_model(A							,output_hidden_states=A							)
	lowercase						:		Optional[Any]   					=		(
	    our_outputs.logits if isinstance(A							,A							) else our_outputs.last_hidden_state
	)

	lowercase						:		Union[str, Any]   					=		from_model(A							)
	lowercase						:		Dict   					=		from_output[-1] if type(A							) 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:
		lowercase						:		List[Any]   					=		our_outputs.hidden_states[-1]

	assert torch.allclose(A							,A							), "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=A							,)

		lowercase						:		Any   					=		2_2_4 if "seer" not in name else 3_8_4
		# we can use the convnext one
		lowercase						:		Dict   					=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"							,size=A							)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name							,commit_message="Add image processor"							,use_temp_dir=A							,)

		print(F'''Pushed {name}'''							)




def 	_A  (       A							,A = None							,A = True							)  ->				Dict:
	lowercase						:		Union[str, Any]   					=		"imagenet-1k-id2label.json"
	lowercase						:		str   					=		1_0_0_0
	lowercase						:		int   					=		(1, num_labels)

	lowercase						:		Dict   					=		"huggingface/label-files"
	lowercase						:		Optional[int]   					=		num_labels
	lowercase						:		List[Any]   					=		json.load(open(cached_download(hf_hub_url(A							,A							,repo_type="dataset"							)							)							,"r"							)							)
	lowercase						:		Union[str, Any]   					=		{int(A							): v for k, v in idalabel.items()}

	lowercase						:		Any   					=		idalabel
	lowercase						:		Dict   					=		{v: k for k, v in idalabel.items()}

	lowercase						:		Any   					=		partial(A							,num_labels=A							,idalabel=A							,labelaid=A							)

	lowercase						:		Any   					=		{
	    "regnet-x-002": ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]							,hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]							,groups_width=8							,layer_type="x"							),
	    "regnet-x-004": ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 1_2]							,hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]							,groups_width=1_6							,layer_type="x"							),
	    "regnet-x-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]							,hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]							,groups_width=2_4							,layer_type="x"							),
	    "regnet-x-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]							,hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]							,groups_width=1_6							,layer_type="x"							),
	    "regnet-x-016": ImageNetPreTrainedConfig(
	        depths=[2, 4, 1_0, 2]							,hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]							,groups_width=2_4							,layer_type="x"							),
	    "regnet-x-032": ImageNetPreTrainedConfig(
	        depths=[2, 6, 1_5, 2]							,hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]							,groups_width=4_8							,layer_type="x"							),
	    "regnet-x-040": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_4, 2]							,hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]							,groups_width=4_0							,layer_type="x"							),
	    "regnet-x-064": ImageNetPreTrainedConfig(
	        depths=[2, 4, 1_0, 1]							,hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]							,groups_width=5_6							,layer_type="x"							),
	    "regnet-x-080": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_5, 1]							,hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]							,groups_width=1_2_0							,layer_type="x"							),
	    "regnet-x-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_1, 1]							,hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]							,groups_width=1_1_2							,layer_type="x"							),
	    "regnet-x-160": ImageNetPreTrainedConfig(
	        depths=[2, 6, 1_3, 1]							,hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]							,groups_width=1_2_8							,layer_type="x"							),
	    "regnet-x-320": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_3, 1]							,hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]							,groups_width=1_6_8							,layer_type="x"							),
	    # y variant
	    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]							,hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]							,groups_width=8							),
	    "regnet-y-004": ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]							,hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]							,groups_width=8							),
	    "regnet-y-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]							,hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]							,groups_width=1_6							),
	    "regnet-y-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]							,hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]							,groups_width=1_6							),
	    "regnet-y-016": ImageNetPreTrainedConfig(
	        depths=[2, 6, 1_7, 2]							,hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]							,groups_width=2_4							),
	    "regnet-y-032": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_3, 1]							,hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]							,groups_width=2_4							),
	    "regnet-y-040": ImageNetPreTrainedConfig(
	        depths=[2, 6, 1_2, 2]							,hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]							,groups_width=6_4							),
	    "regnet-y-064": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_4, 2]							,hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]							,groups_width=7_2							),
	    "regnet-y-080": ImageNetPreTrainedConfig(
	        depths=[2, 4, 1_0, 1]							,hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]							,groups_width=5_6							),
	    "regnet-y-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_1, 1]							,hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]							,groups_width=1_1_2							),
	    "regnet-y-160": ImageNetPreTrainedConfig(
	        depths=[2, 4, 1_1, 1]							,hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]							,groups_width=1_1_2							),
	    "regnet-y-320": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_2, 1]							,hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,groups_width=2_3_2							),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 1_2, 1]							,hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,groups_width=2_3_2							),
	    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 1_2, 1]							,hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]							,groups_width=3_2_8							),
	    "regnet-y-1280-seer": RegNetConfig(
	        depths=[2, 7, 1_7, 1]							,hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]							,groups_width=2_6_4							),
	    "regnet-y-2560-seer": RegNetConfig(
	        depths=[3, 7, 1_6, 1]							,hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]							,groups_width=6_4_0							),
	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]							,hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]							,groups_width=1_0_1_0							),
	    # finetuned on imagenet
	    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_2, 1]							,hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,groups_width=2_3_2							),
	    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 1_2, 1]							,hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]							,groups_width=3_2_8							),
	    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]							,hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]							,groups_width=2_6_4							),
	    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[3, 7, 1_6, 1]							,hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]							,groups_width=6_4_0							),
	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]							,hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]							,groups_width=1_0_1_0							),
	}

	lowercase						:		List[str]   					=		NameToOurModelFuncMap()
	lowercase						:		Tuple   					=		NameToFromModelFuncMap()
	# add seer weights logic

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

	# pretrained
	lowercase						:		str   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,)

	lowercase						:		Dict   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,)

	lowercase						:		Tuple   					=		partial(
	    A							,"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()							)							,)

	lowercase						:		List[Any]   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"							,lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=2_7							,group_width=1_0_1_0							,w_a=1_7_4_4							,w_a=620.83							,w_m=2.52							)							)							)							,)

	# IN1K finetuned
	lowercase						:		Any   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,)

	lowercase						:		Union[str, Any]   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,)

	lowercase						:		Union[str, Any]   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"							,lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)							,)

	lowercase						:		str   					=		partial(
	    A							,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"							,lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=2_7							,group_width=1_0_1_0							,w_a=1_7_4_4							,w_a=620.83							,w_m=2.52							)							)							)							,)

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


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				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.""",
					)

					lowerCAmelCase   : List[str]             =				parser.parse_args()

					lowerCAmelCase   : Path             =				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)






'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


