





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_    =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_    =    []
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
	    )
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  state_dict.pop(_UpperCamelCase			)
				snake_case_						:   Any           =  val






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case_						:   Optional[int]           =  key.replace('''backbone.0.body'''			,    '''backbone.conv_encoder.model'''			)
												snake_case_						:   List[Any]           =  value
								else:
												snake_case_						:   List[Any]           =  value

				return new_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  ''''''
				if is_panoptic:
								snake_case_						:   Tuple           =  '''conditional_detr.'''

				# first: transformer encoder
				for i in range(6			):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								snake_case_						:   Dict           =  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''			)
								snake_case_						:   Tuple           =  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''			)
								# next, add query, keys and values (in that order) to the state dict
								snake_case_						:   int           =  in_proj_weight[:256, :]
								snake_case_						:   List[str]           =  in_proj_bias[:256]
								snake_case_						:   Tuple           =  in_proj_weight[256:512, :]
								snake_case_						:   Dict           =  in_proj_bias[256:512]
								snake_case_						:   Optional[int]           =  in_proj_weight[-256:, :]
								snake_case_						:   Optional[Any]           =  in_proj_bias[-256:]






def 					lowerCamelCase_						(    )		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:   Dict           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)

				return im






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								snake_case_						:   List[str]           =  '''resnet101'''
				if "dc5" in model_name:
								snake_case_						:   Dict           =  True
				snake_case_						:   Optional[Any]           =  '''panoptic''' in model_name
				if is_panoptic:
								snake_case_						:   List[str]           =  250
				else:
								snake_case_						:   Union[str, Any]           =  91
								snake_case_						:   Dict           =  '''huggingface/label-files'''
								snake_case_						:   Optional[Any]           =  '''coco-detection-id2label.json'''
								snake_case_						:   str           =  json.load(open(hf_hub_download(_UpperCamelCase			,    _UpperCamelCase			,    repo_type='''dataset'''			)			,    '''r'''			)			)
								snake_case_						:   int           =  {int(_UpperCamelCase			): v for k, v in idalabel.items()}
								snake_case_						:   Tuple           =  idalabel
								snake_case_						:   Any           =  {v: k for k, v in idalabel.items()}

				# load image processor
				snake_case_						:   List[str]           =  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				snake_case_						:   Union[str, Any]           =  ConditionalDetrImageProcessor(format=_UpperCamelCase			)

				# prepare image
				snake_case_						:   Union[str, Any]           =  prepare_img()
				snake_case_						:   Tuple           =  image_processor(images=_UpperCamelCase			,    return_tensors='''pt'''			)
				snake_case_						:   Any           =  encoding['''pixel_values''']

				logger.info(f'''Converting model {model_name}...'''			)

				# load original model from torch hub
				snake_case_						:   Union[str, Any]           =  torch.hub.load('''DeppMeng/ConditionalDETR'''			,    _UpperCamelCase			,    pretrained=_UpperCamelCase			).eval()
				snake_case_						:   str           =  conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case_						:   Optional[Any]           =  '''conditional_detr.''' + src
								rename_key(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  rename_backbone_keys(_UpperCamelCase			)
				# query, key and value matrices need special treatment
				read_in_q_k_v(_UpperCamelCase			,    is_panoptic=_UpperCamelCase			)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				snake_case_						:   Optional[int]           =  '''conditional_detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''conditional_detr'''			)
												    and not key.startswith('''class_labels_classifier'''			)
												    and not key.startswith('''bbox_predictor'''			)
												):
																snake_case_						:   str           =  state_dict.pop(_UpperCamelCase			)
																snake_case_						:   Union[str, Any]           =  val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case_						:   Optional[Any]           =  state_dict.pop(_UpperCamelCase			)
																snake_case_						:   Union[str, Any]           =  val
												elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
																continue
												else:
																snake_case_						:   Any           =  state_dict.pop(_UpperCamelCase			)
																snake_case_						:   List[str]           =  val
								else:
												if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
																snake_case_						:   Any           =  state_dict.pop(_UpperCamelCase			)
																snake_case_						:   Tuple           =  val
    # finally, create HuggingFace model and load state dict
				snake_case_						:   int           =  ConditionalDetrForSegmentation(_UpperCamelCase			) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase			)
				model.load_state_dict(_UpperCamelCase			)
				model.eval()
				model.push_to_hub(repo_id=_UpperCamelCase			,    organization='''DepuMeng'''			,    commit_message='''Add model'''			)
				# verify our conversion
				snake_case_						:   int           =  conditional_detr(_UpperCamelCase			)
				snake_case_						:   Optional[int]           =  model(_UpperCamelCase			)
				assert torch.allclose(outputs.logits			,    original_outputs['''pred_logits''']			,    atol=1E-4			)
				assert torch.allclose(outputs.pred_boxes			,    original_outputs['''pred_boxes''']			,    atol=1E-4			)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks			,    original_outputs['''pred_masks''']			,    atol=1E-4			)

				# Save model and image processor
				logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''			)
				Path(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
				model.save_pretrained(_UpperCamelCase			)
				image_processor.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
	lowerCAmelCase_    =    argparse.ArgumentParser()

	parser.add_argument(
	    '''--model_name''',
	    default='''conditional_detr_resnet50''',
	    type=str,
	    help='''Name of the CONDITIONAL_DETR 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.'''
	)
	lowerCAmelCase_    =    parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
	    FlaxAlbertForMaskedLM,
	    FlaxAlbertForMultipleChoice,
	    FlaxAlbertForPreTraining,
	    FlaxAlbertForQuestionAnswering,
	    FlaxAlbertForSequenceClassification,
	    FlaxAlbertForTokenClassification,
	    FlaxAlbertModel,
	)



class  __lowerCAmelCase	(      unittest.TestCase					):






			def __init__(self ,							__magic_name__ ,							__magic_name__=13 ,							__magic_name__=7 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=99 ,							__magic_name__=32 ,							__magic_name__=5 ,							__magic_name__=4 ,							__magic_name__=37 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=512 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=0.02 ,							__magic_name__=4 ,							)					->       Tuple:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



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

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

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

							snake_case_						:   List[Any]           =  AlbertConfig(
							    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=__magic_name__ ,							initializer_range=self.initializer_range ,							)

							return config, input_ids, token_type_ids, attention_mask






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.prepare_config_and_inputs()
							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  config_and_inputs
							snake_case_						:   int           =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict




@require_flax
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					Optional[int]	      =				(
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






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


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  FlaxAlbertModelTester(self   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							for model_class_name in self.all_model_classes:
											snake_case_						:   Dict           =  model_class_name.from_pretrained('''albert-base-v2'''   )
											snake_case_						:   Union[str, Any]           =  model(np.ones((1, 1)   )   )
											self.assertIsNotNone(__magic_name__   )




@require_flax
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  FlaxAlbertModel.from_pretrained('''albert-base-v2'''   )
							snake_case_						:   Optional[int]           =  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
							snake_case_						:   Dict           =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
							snake_case_						:   Union[str, Any]           =  model(__magic_name__ ,							attention_mask=__magic_name__   )[0]
							snake_case_						:   Tuple           =  (1, 11, 768)
							self.assertEqual(output.shape ,							__magic_name__   )
							snake_case_						:   str           =  np.array(
							    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]   )

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