



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase       :					List[Any]      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			SwinConfig(
						    embed_dim=1_9_2					, depths=(2, 2, 1_8, 2)					, num_heads=(6, 1_2, 2_4, 4_8)					, window_size=1_2					, out_features=["""stage2""", """stage3""", """stage4"""]					, )

						snake_case_   : Optional[Any]						=			DetaConfig(
						    backbone_config=__UpperCamelCase					, num_queries=9_0_0					, encoder_ffn_dim=2_0_4_8					, decoder_ffn_dim=2_0_4_8					, num_feature_levels=5					, assign_first_stage=__UpperCamelCase					, with_box_refine=__UpperCamelCase					, two_stage=__UpperCamelCase					, )

						# set labels
						snake_case_   : str						=			"""huggingface/label-files"""
						if "o365" in model_name:
												snake_case_   : List[str]						=			3_6_6
												snake_case_   : Dict						=			"""object365-id2label.json"""
						else:
												snake_case_   : Optional[Any]						=			9_1
												snake_case_   : int						=			"""coco-detection-id2label.json"""

						snake_case_   : Optional[Any]						=			num_labels
						snake_case_   : int						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : Optional[int]						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
						snake_case_   : Dict						=			idalabel
						snake_case_   : Any						=			{v: k for k, v in idalabel.items()}

						return config
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			[]

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")    )
						rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")    )
						rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")    )
						rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")    )
						# stages
						for i in range(len(config.backbone_config.depths    )    ):
												for j in range(config.backbone_config.depths[i]    ):
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')    )

												if i < 3:
																		rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')    )
																		rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')    )

						rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")    )
						rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")    )
						rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")    )
						rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")    )
						rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")    )
						rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")    )

						# transformer encoder
						for i in range(config.encoder_layers    ):
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')    )
												rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')    )

						# transformer decoder
						for i in range(config.decoder_layers    ):
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')    )
												rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')    )

						# fmt: on

						return rename_keys
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			dct.pop(__UpperCamelCase    )
						snake_case_   : Tuple						=			val
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
						for i in range(len(backbone_config.depths    )    ):
												snake_case_   : Union[str, Any]						=			num_features[i]
												for j in range(backbone_config.depths[i]    ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		snake_case_   : Optional[int]						=			state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'    )
																		snake_case_   : Optional[int]						=			state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'    )
																		# next, add query, keys and values (in that order) to the state dict
																		snake_case_   : Union[str, Any]						=			in_proj_weight[:dim, :]
																		snake_case_   : List[str]						=			in_proj_bias[: dim]
																		snake_case_   : str						=			in_proj_weight[
																		    dim : dim * 2, :
																		]
																		snake_case_   : Any						=			in_proj_bias[
																		    dim : dim * 2
																		]
																		snake_case_   : Any						=			in_proj_weight[
																		    -dim :, :
																		]
																		snake_case_   : str						=			in_proj_bias[-dim :]
																		# fmt: on
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			config.d_model
						for i in range(config.decoder_layers    ):
												# read in weights + bias of input projection layer of self-attention
												snake_case_   : List[str]						=			state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'    )
												snake_case_   : str						=			state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'    )
												# next, add query, keys and values (in that order) to the state dict
												snake_case_   : Optional[Any]						=			in_proj_weight[:hidden_size, :]
												snake_case_   : Union[str, Any]						=			in_proj_bias[:hidden_size]
												snake_case_   : Tuple						=			in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												snake_case_   : Union[str, Any]						=			in_proj_bias[hidden_size : hidden_size * 2]
												snake_case_   : Union[str, Any]						=			in_proj_weight[-hidden_size:, :]
												snake_case_   : List[Any]						=			in_proj_bias[-hidden_size:]
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_   : Union[str, Any]						=			Image.open(requests.get(__UpperCamelCase					, stream=__UpperCamelCase    ).raw    )

						return im
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			get_deta_config(__UpperCamelCase    )

						# load original state dict
						if model_name == "deta-swin-large":
												snake_case_   : Optional[Any]						=			hf_hub_download(repo_id="""nielsr/deta-checkpoints"""					, filename="""adet_swin_ft.pth"""    )
						elif model_name == "deta-swin-large-o365":
												snake_case_   : Any						=			hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""					, filename="""deta_swin_pt_o365.pth"""    )
						else:
												raise ValueError(F'Model name {model_name} not supported'    )

						snake_case_   : int						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )["""model"""]

						# original state dict
						for name, param in state_dict.items():
												print(__UpperCamelCase					, param.shape    )

						# rename keys
						snake_case_   : Union[str, Any]						=			create_rename_keys(__UpperCamelCase    )
						for src, dest in rename_keys:
												rename_key(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						read_in_swin_q_k_v(__UpperCamelCase					, config.backbone_config    )
						read_in_decoder_q_k_v(__UpperCamelCase					, __UpperCamelCase    )

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		snake_case_   : Optional[int]						=			state_dict.pop(__UpperCamelCase    )
																		snake_case_   : Tuple						=			val
												if "input_proj" in key:
																		snake_case_   : Dict						=			state_dict.pop(__UpperCamelCase    )
																		snake_case_   : List[Any]						=			val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		snake_case_   : Optional[Any]						=			state_dict.pop(__UpperCamelCase    )
																		snake_case_   : Union[str, Any]						=			val

    # finally, create HuggingFace model and load state dict
						snake_case_   : List[str]						=			DetaForObjectDetection(__UpperCamelCase    )
						model.load_state_dict(__UpperCamelCase    )
						model.eval()

						snake_case_   : int						=			"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(__UpperCamelCase    )

						# load image processor
						snake_case_   : Any						=			DetaImageProcessor(format="""coco_detection"""    )

						# verify our conversion on image
						snake_case_   : Dict						=			prepare_img()
						snake_case_   : str						=			processor(images=__UpperCamelCase					, return_tensors="""pt"""    )
						snake_case_   : Optional[Any]						=			encoding["""pixel_values"""]
						snake_case_   : int						=			model(pixel_values.to(__UpperCamelCase    )    )

						# verify logits
						print("""Logits:"""					, outputs.logits[0, :3, :3]    )
						print("""Boxes:"""					, outputs.pred_boxes[0, :3, :3]    )
						if model_name == "deta-swin-large":
												snake_case_   : Any						=			torch.tensor(
												    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]    )
												snake_case_   : Optional[int]						=			torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]    )
						elif model_name == "deta-swin-large-o365":
												snake_case_   : Dict						=			torch.tensor(
												    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]    )
												snake_case_   : Optional[Any]						=			torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]    )
						assert torch.allclose(outputs.logits[0, :3, :3]					, expected_logits.to(__UpperCamelCase    )					, atol=1E-4    )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]					, expected_boxes.to(__UpperCamelCase    )					, atol=1E-4    )
						print("""Everything ok!"""    )

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'    )
												Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
												model.save_pretrained(__UpperCamelCase    )
												processor.save_pretrained(__UpperCamelCase    )

						# Push to hub
						if push_to_hub:
												print("""Pushing model and processor to hub..."""    )
												model.push_to_hub(F'jozhang97/{model_name}'    )
												processor.push_to_hub(F'jozhang97/{model_name}'    )


if __name__ == "__main__":
					__lowerCAmelCase       :					List[str]      					=					argparse.ArgumentParser()

					parser.add_argument(
					    '''--model_name''',
					    type=str,
					    default='''deta-swin-large''',
					    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
					    help='''Name of the model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''',
					    default=None,
					    type=str,
					    help='''Path to the folder to output PyTorch model.''',
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)
					__lowerCAmelCase       :					Union[str, Any]      					=					parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Tuple						=			parent
								snake_case_   : List[str]						=			batch_size
								snake_case_   : int						=			seq_length
								snake_case_   : List[Any]						=			is_training
								snake_case_   : Optional[int]						=			use_attention_mask
								snake_case_   : Optional[Any]						=			use_token_type_ids
								snake_case_   : Union[str, Any]						=			use_labels
								snake_case_   : str						=			vocab_size
								snake_case_   : List[str]						=			hidden_size
								snake_case_   : List[str]						=			num_hidden_layers
								snake_case_   : int						=			num_attention_heads
								snake_case_   : Dict						=			intermediate_size
								snake_case_   : Union[str, Any]						=			hidden_act
								snake_case_   : List[str]						=			hidden_dropout_prob
								snake_case_   : Any						=			attention_probs_dropout_prob
								snake_case_   : List[str]						=			max_position_embeddings
								snake_case_   : Union[str, Any]						=			type_vocab_size
								snake_case_   : str						=			type_sequence_label_size
								snake_case_   : Dict						=			initializer_range
								snake_case_   : str						=			num_choices





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

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

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

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

								snake_case_   : Union[str, Any]						=			RoFormerConfig(
								    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=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : str						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : Tuple						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict




@require_flax
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,    from_pt=_lowercase   )
														snake_case_   : List[str]						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
								snake_case_   : Tuple						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
								snake_case_   : Dict						=			model(_lowercase   )[0]

								snake_case_   : Optional[int]						=			5_0_0_0_0

								snake_case_   : Union[str, Any]						=			(1, 6, vocab_size)
								self.assertEqual(output.shape							,    _lowercase   )

								snake_case_   : Dict						=			jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

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

