


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

SCREAMING_SNAKE_CASE :   Tuple							      =				logging.get_logger(__name__)






@add_end_docstrings(_a     )
class _lowerCamelCase(		_a     ):
       def __init__(      self,   **lowerCamelCase) ->     List[str]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           requires_backends(self,   'vision')
           requires_backends(self,   'torch')

           if self.framework != "pt":
               raise ValueError(F'''The {self.__class__} is only available in PyTorch.''')

           self.check_model_type(lowerCamelCase)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	{}
           _lowercase :							List[Any]   	=	{}
           _lowercase :							str   	=	{}
           # preprocess args
           if "points_per_batch" in kwargs:
               _lowercase :							Any   	=	kwargs['points_per_batch']
           if "points_per_crop" in kwargs:
               _lowercase :							Tuple   	=	kwargs['points_per_crop']
           if "crops_n_layers" in kwargs:
               _lowercase :							int   	=	kwargs['crops_n_layers']
           if "crop_overlap_ratio" in kwargs:
               _lowercase :							int   	=	kwargs['crop_overlap_ratio']
           if "crop_n_points_downscale_factor" in kwargs:
               _lowercase :							int   	=	kwargs['crop_n_points_downscale_factor']
           # postprocess args
           if "pred_iou_thresh" in kwargs:
               _lowercase :							Dict   	=	kwargs['pred_iou_thresh']
           if "stability_score_offset" in kwargs:
               _lowercase :							Dict   	=	kwargs['stability_score_offset']
           if "mask_threshold" in kwargs:
               _lowercase :							Optional[int]   	=	kwargs['mask_threshold']
           if "stability_score_thresh" in kwargs:
               _lowercase :							List[Any]   	=	kwargs['stability_score_thresh']
           if "crops_nms_thresh" in kwargs:
               _lowercase :							Dict   	=	kwargs['crops_nms_thresh']
           if "output_rle_mask" in kwargs:
               _lowercase :							Tuple   	=	kwargs['output_rle_mask']
           if "output_bboxes_mask" in kwargs:
               _lowercase :							List[str]   	=	kwargs['output_bboxes_mask']
           return preprocess_kwargs, forward_params, postprocess_kwargs
       def __call__(      self,   lowerCamelCase,   *lowerCamelCase,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           return super().__call__(lowerCamelCase,   *lowerCamelCase,   num_workers=lowerCamelCase,   batch_size=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=64,   lowerCamelCase = 0,   lowerCamelCase = 5_12 / 15_00,   lowerCamelCase = 32,   lowerCamelCase = 1,   ) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	load_image(lowerCamelCase)
           _lowercase :							Any   	=	self.image_processor.size['longest_edge']
           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							List[Any]   	=	self.image_processor.generate_crop_boxes(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	self.image_processor(images=lowerCamelCase,   return_tensors='pt')

           with self.device_placement():
               if self.framework == "pt":
                   _lowercase :							int   	=	self.get_inference_context()
                   with inference_context():
                       _lowercase :							Optional[Any]   	=	self._ensure_tensor_on_device(lowerCamelCase,   device=self.device)
                       _lowercase :							Any   	=	self.model.get_image_embeddings(model_inputs.pop('pixel_values'))
                       _lowercase :							Dict   	=	image_embeddings

           _lowercase :							Union[str, Any]   	=	grid_points.shape[1]
           _lowercase :							Dict   	=	points_per_batch if points_per_batch is not None else n_points

           if points_per_batch <= 0:
               raise ValueError(
                   'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
                   'To return all points at once, set points_per_batch to None')

           for i in range(0,   lowerCamelCase,   lowerCamelCase):
               _lowercase :							Any   	=	grid_points[:, i : i + points_per_batch, :, :]
               _lowercase :							Union[str, Any]   	=	input_labels[:, i : i + points_per_batch]
               _lowercase :							List[Any]   	=	i == n_points - points_per_batch
               yield {
                   "input_points": batched_points,
                   "input_labels": labels,
                   "input_boxes": crop_boxes,
                   "is_last": is_last,
                   **model_inputs,
               }
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=0.8_8,   lowerCamelCase=0.9_5,   lowerCamelCase=0,   lowerCamelCase=1,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	model_inputs.pop('input_boxes')
           _lowercase :							Dict   	=	model_inputs.pop('is_last')
           _lowercase :							List[Any]   	=	model_inputs.pop('original_sizes').tolist()
           _lowercase :							List[Any]   	=	model_inputs.pop('reshaped_input_sizes').tolist()

           _lowercase :							Tuple   	=	self.model(**lowerCamelCase)

           # post processing happens here in order to avoid CPU GPU copies of ALL the masks
           _lowercase :							Dict   	=	model_outputs['pred_masks']
           _lowercase :							Optional[int]   	=	self.image_processor.post_process_masks(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   binarize=lowerCamelCase)
           _lowercase :							Tuple   	=	model_outputs['iou_scores']
           _lowercase	,       _lowercase	,       _lowercase :							str   	=	self.image_processor.filter_masks(
               masks[0],   iou_scores[0],   original_sizes[0],   input_boxes[0],   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   )
           return {
               "masks": masks,
               "is_last": is_last,
               "boxes": boxes,
               "iou_scores": iou_scores,
           }




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False,   lowerCamelCase=False,   lowerCamelCase=0.7,   ) ->     Any:




           """simple docstring"""

           _lowercase :							Tuple   	=	[]
           _lowercase :							Optional[int]   	=	[]
           _lowercase :							str   	=	[]
           for model_output in model_outputs:
               all_scores.append(model_output.pop('iou_scores'))
               all_masks.extend(model_output.pop('masks'))
               all_boxes.append(model_output.pop('boxes'))

           _lowercase :							Dict   	=	torch.cat(lowerCamelCase)
           _lowercase :							Dict   	=	torch.cat(lowerCamelCase)
           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							List[Any]   	=	self.image_processor.post_process_for_mask_generation(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)

           _lowercase :							Optional[Any]   	=	defaultdict(lowerCamelCase)
           for output in model_outputs:
               for k, v in output.items():
                   extra[k].append(lowerCamelCase)

           _lowercase :							Optional[int]   	=	{}
           if output_rle_mask:
               _lowercase :							Any   	=	rle_mask

           if output_bboxes_mask:
               _lowercase :							Dict   	=	bounding_boxes

           return {"masks": output_masks, "scores": iou_scores, **optional, **extra}






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 _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=2,   lowerCamelCase=8,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=16,   lowerCamelCase=5,   lowerCamelCase=2,   lowerCamelCase=36,   lowerCamelCase="gelu",   lowerCamelCase=0.0,   lowerCamelCase=0.0,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Dict:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	parent
           _lowercase :							List[str]   	=	batch_size
           _lowercase :							Dict   	=	seq_length
           _lowercase :							List[str]   	=	is_training
           _lowercase :							Any   	=	use_input_mask
           _lowercase :							Any   	=	use_token_type_ids
           _lowercase :							Optional[int]   	=	use_labels
           _lowercase :							str   	=	vocab_size
           _lowercase :							str   	=	hidden_size
           _lowercase :							str   	=	num_hidden_layers
           _lowercase :							List[Any]   	=	num_attention_heads
           _lowercase :							Union[str, Any]   	=	intermediate_size
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							Optional[int]   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							str   	=	type_vocab_size
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							Optional[Any]   	=	initializer_range
           _lowercase :							Dict   	=	num_labels
           _lowercase :							Dict   	=	num_choices
           _lowercase :							int   	=	scope
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							str   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							List[Any]   	=	None
           if self.use_input_mask:
               _lowercase :							List[str]   	=	random_attention_mask([self.batch_size, self.seq_length])

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

           _lowercase :							Optional[Any]   	=	None
           _lowercase :							Optional[int]   	=	None
           _lowercase :							Dict   	=	None
           if self.use_labels:
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size, self.seq_length],   self.num_labels)
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size],   self.num_choices)

           _lowercase :							Optional[int]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     int:




           """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=lowerCamelCase,   initializer_range=self.initializer_range,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Any   	=	self.get_config()
           _lowercase :							List[str]   	=	3_00
           return config
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	self.prepare_config_and_inputs()

           _lowercase :							Tuple   	=	True
           _lowercase :							Any   	=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase :							Union[str, Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	MraModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Dict   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							Tuple   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	True
           _lowercase :							List[Any]   	=	MraModel(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Tuple   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   encoder_hidden_states=lowerCamelCase,   encoder_attention_mask=lowerCamelCase,   )
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   encoder_hidden_states=lowerCamelCase,   )
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	MraForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	MraForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   )
           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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							str   	=	self.num_labels
           _lowercase :							List[Any]   	=	MraForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.num_labels
           _lowercase :							str   	=	MraForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Any   	=	self.num_choices
           _lowercase :							Optional[Any]   	=	MraForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							Dict   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							Dict   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							Tuple   	=	config_and_inputs
           _lowercase :							Optional[Any]   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Optional[int]									=    (
           (
               MraModel,
               MraForMaskedLM,
               MraForMultipleChoice,
               MraForQuestionAnswering,
               MraForSequenceClassification,
               MraForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Optional[int]									=    False
       lowercase_				:			Optional[int]									=    False
       lowercase_				:			int									=    False
       lowercase_				:			Optional[Any]									=    False

       lowercase_				:			Any									=    ()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	MraModelTester(self)
           _lowercase :							Any   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               _lowercase :							Optional[int]   	=	type
               self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							List[str]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							List[str]   	=	MraModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @unittest.skip(reason='MRA does not output attentions')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           return






@require_torch
class _lowerCamelCase(		unittest.TestCase     ):
       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

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

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Union[str, Any]   	=	torch.Size((1, 2_56, 7_68))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Dict   	=	torch.tensor(
               [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]])

           self.assertTrue(torch.allclose(output[:, :3, :3],   lowerCamelCase,   atol=1E-4))
       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[str]   	=	MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4')
           _lowercase :							Optional[int]   	=	torch.arange(2_56).unsqueeze(0)

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Union[str, Any]   	=	5_02_65

           _lowercase :							int   	=	torch.Size((1, 2_56, vocab_size))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Optional[Any]   	=	torch.tensor(
               [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]])

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




       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Any   	=	MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3')
           _lowercase :							Optional[int]   	=	torch.arange(40_96).unsqueeze(0)

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Optional[Any]   	=	5_02_65

           _lowercase :							Union[str, Any]   	=	torch.Size((1, 40_96, vocab_size))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Any   	=	torch.tensor(
               [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]])

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


