

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()
A											=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A											=     []
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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= state_dict.pop(lowercase__	)
	snake_case_      		= val





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case_      		= key.replace('backbone.0.body'			, 'backbone.conv_encoder.model'	)
			snake_case_      		= value
		else:
			snake_case_      		= value

	return new_state_dict





def a(lowercase__			, lowercase__=False	):





	'''simple docstring'''
	snake_case_      		= ''
	if is_panoptic:
		snake_case_      		= '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_      		= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
		snake_case_      		= 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_      		= in_proj_weight[:256, :]
		snake_case_      		= in_proj_bias[:256]
		snake_case_      		= in_proj_weight[256:512, :]
		snake_case_      		= in_proj_bias[256:512]
		snake_case_      		= in_proj_weight[-256:, :]
		snake_case_      		= in_proj_bias[-256:]





def a():





	'''simple docstring'''
	snake_case_      		= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_      		= Image.open(requests.get(lowercase__			, stream=lowercase__	).raw	)

	return im





@torch.no_grad()
def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		snake_case_      		= 'resnet101'
	if "dc5" in model_name:
		snake_case_      		= True
	snake_case_      		= 'panoptic' in model_name
	if is_panoptic:
		snake_case_      		= 250
	else:
		snake_case_      		= 91
		snake_case_      		= 'huggingface/label-files'
		snake_case_      		= 'coco-detection-id2label.json'
		snake_case_      		= json.load(open(hf_hub_download(lowercase__			, lowercase__			, repo_type='dataset'	)			, 'r'	)	)
		snake_case_      		= {int(lowercase__	): v for k, v in idalabel.items()}
		snake_case_      		= idalabel
		snake_case_      		= {v: k for k, v in idalabel.items()}

	# load image processor
	snake_case_      		= 'coco_panoptic' if is_panoptic else 'coco_detection'
	snake_case_      		= ConditionalDetrImageProcessor(format=lowercase__	)

	# prepare image
	snake_case_      		= prepare_img()
	snake_case_      		= image_processor(images=lowercase__			, return_tensors='pt'	)
	snake_case_      		= encoding['pixel_values']

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

	# load original model from torch hub
	snake_case_      		= torch.hub.load('DeppMeng/ConditionalDETR'			, lowercase__			, pretrained=lowercase__	).eval()
	snake_case_      		= conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case_      		= 'conditional_detr.' + src
		rename_key(lowercase__			, lowercase__			, lowercase__	)
	snake_case_      		= rename_backbone_keys(lowercase__	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(lowercase__			, is_panoptic=lowercase__	)
	# 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_      		= '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_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
    # finally, create HuggingFace model and load state dict
	snake_case_      		= ConditionalDetrForSegmentation(lowercase__	) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__	)
	model.load_state_dict(lowercase__	)
	model.eval()
	model.push_to_hub(repo_id=lowercase__			, organization='DepuMeng'			, commit_message='Add model'	)
	# verify our conversion
	snake_case_      		= conditional_detr(lowercase__	)
	snake_case_      		= model(lowercase__	)
	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(lowercase__	).mkdir(exist_ok=lowercase__	)
	model.save_pretrained(lowercase__	)
	image_processor.save_pretrained(lowercase__	)


if __name__ == "__main__":
		A											=     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.'
		)
		A											=     parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








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							)							)
