






'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

      if is_vision_available():
            from transformers import MaskFormerImageProcessor

if is_vision_available():
      from PIL import Image




class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=2			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=10			,     __lowerCAmelCase=3			,     __lowerCAmelCase=32 * 4			,     __lowerCAmelCase=32 * 6			,     __lowerCAmelCase=4			,     __lowerCAmelCase=32			,     ):
       UpperCamelCase_				:   List[str]					    =							parent
       UpperCamelCase_				:   List[Any]					    =							batch_size
       UpperCamelCase_				:   int					    =							is_training
       UpperCamelCase_				:   Union[str, Any]					    =							use_auxiliary_loss
       UpperCamelCase_				:   Optional[Any]					    =							num_queries
       UpperCamelCase_				:   List[str]					    =							num_channels
       UpperCamelCase_				:   Optional[int]					    =							min_size
       UpperCamelCase_				:   Union[str, Any]					    =							max_size
       UpperCamelCase_				:   Optional[int]					    =							num_labels
       UpperCamelCase_				:   List[Any]					    =							mask_feature_size
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
           __lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							torch.ones([self.batch_size, self.min_size, self.max_size]			,     device=__lowerCAmelCase )

       UpperCamelCase_				:   Dict					    =							(
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,     device=__lowerCAmelCase ) > 0.5
       ).float()
       UpperCamelCase_				:   List[Any]					    =							(torch.rand((self.batch_size, self.num_labels)			,     device=__lowerCAmelCase ) > 0.5).long()

       UpperCamelCase_				:   Dict					    =							self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels
 def   _UpperCAmelCase       (							self ):
       return MaskFormerConfig.from_backbone_and_decoder_configs(
           backbone_config=SwinConfig(
               depths=[1, 1, 1, 1]			,     )			,     decoder_config=DetrConfig(
               decoder_ffn_dim=1_28			,     num_queries=self.num_queries			,     decoder_attention_heads=2			,     d_model=self.mask_feature_size			,     )			,     mask_feature_size=self.mask_feature_size			,     fpn_feature_size=self.mask_feature_size			,     num_channels=self.num_channels			,     num_labels=self.num_labels			,     )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							self.prepare_config_and_inputs()
       UpperCamelCase_				:   int					    =							{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
       return config, inputs_dict
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							output.encoder_hidden_states
       UpperCamelCase_				:   Any					    =							output.pixel_decoder_hidden_states
       UpperCamelCase_				:   Union[str, Any]					    =							output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(__lowerCAmelCase )			,     len(config.backbone_config.depths ) )
       self.parent.assertTrue(len(__lowerCAmelCase )			,     len(config.backbone_config.depths ) )
       self.parent.assertTrue(len(__lowerCAmelCase )			,     config.decoder_config.decoder_layers )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       with torch.no_grad():
             UpperCamelCase_				:   Tuple					    =							MaskFormerModel(config=__lowerCAmelCase )
             model.to(__lowerCAmelCase )
             model.eval()

             UpperCamelCase_				:   str					    =							model(pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase )
             UpperCamelCase_				:   Union[str, Any]					    =							model(__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
       # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
       # encoder and pixel decoder
       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape			,     (self.batch_size, self.num_queries, self.mask_feature_size)			,     )
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
       self.parent.assertTrue(output.encoder_last_hidden_state is not None )

       if output_hidden_states:
             self.check_output_hidden_state(__lowerCAmelCase			,     __lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()

       def comm_check_on_output(__lowerCAmelCase ):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
             self.parent.assertTrue(result.encoder_last_hidden_state is not None )
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape			,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)			,     )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape			,     (self.batch_size, self.num_queries, self.num_labels + 1) )

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase )
             UpperCamelCase_				:   List[str]					    =							model(__lowerCAmelCase )

             comm_check_on_output(__lowerCAmelCase )

             UpperCamelCase_				:   Dict					    =							model(
                 pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase )

       comm_check_on_output(__lowerCAmelCase )

       self.parent.assertTrue(result.loss is not None )
       self.parent.assertEqual(result.loss.shape			,     torch.Size([1] ) )






@require_torch
class     A    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Tuple      =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 __a			:						List[str]      =						(
     {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 __a			:						List[Any]      =						False
 __a			:						int      =						False
 __a			:						List[str]      =						False
 __a			:						Any      =						False
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							MaskFormerModelTester(self )
       UpperCamelCase_				:   Optional[int]					    =							ConfigTester(self			,     config_class=__lowerCAmelCase			,     has_text_modality=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       self.config_tester.run_common_tests()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase			,     **__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
 @unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer is not a generative model""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer does not use token embeddings""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @require_torch_multi_gpu
 @unittest.skip(
     reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_				:   List[Any]					    =							model_class(__lowerCAmelCase )
             UpperCamelCase_				:   Union[str, Any]					    =							inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_				:   Optional[Any]					    =							[*signature.parameters.keys()]

             UpperCamelCase_				:   Any					    =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]			,     __lowerCAmelCase )
 @slow
 def   _UpperCAmelCase       (							self ):
       for model_name in ["facebook/maskformer-swin-small-coco"]:
             UpperCamelCase_				:   List[str]					    =							MaskFormerModel.from_pretrained(__lowerCAmelCase )
             self.assertIsNotNone(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							(self.model_tester.min_size,) * 2
       UpperCamelCase_				:   Optional[int]					    =							{
           """pixel_values""": torch.randn((2, 3, *size)			,     device=__lowerCAmelCase ),
           """mask_labels""": torch.randn((2, 10, *size)			,     device=__lowerCAmelCase ),
           """class_labels""": torch.zeros(2			,     10			,     device=__lowerCAmelCase ).long(),
       }

       UpperCamelCase_				:   Union[str, Any]					    =							MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
       UpperCamelCase_				:   List[str]					    =							model(**__lowerCAmelCase )
       self.assertTrue(outputs.loss is not None )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase			,     **__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_				:   Optional[int]					    =							model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
             UpperCamelCase_				:   Optional[int]					    =							model(**__lowerCAmelCase			,     output_attentions=__lowerCAmelCase )
             self.assertTrue(outputs.attentions is not None )
 def   _UpperCAmelCase       (							self ):
       if not self.model_tester.is_training:
             return
       # only MaskFormerForInstanceSegmentation has the loss
       UpperCamelCase_				:   int					    =							self.all_model_classes[1]
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							self.model_tester.prepare_config_and_inputs()

       UpperCamelCase_				:   Dict					    =							model_class(__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.train()

       UpperCamelCase_				:   Union[str, Any]					    =							model(__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase ).loss
       loss.backward()





 def   _UpperCAmelCase       (							self ):
       # only MaskFormerForInstanceSegmentation has the loss
       UpperCamelCase_				:   List[Any]					    =							self.all_model_classes[1]
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.model_tester.prepare_config_and_inputs()
       UpperCamelCase_				:   Optional[int]					    =							True
       UpperCamelCase_				:   List[str]					    =							True

       UpperCamelCase_				:   Any					    =							model_class(__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.train()

       UpperCamelCase_				:   Dict					    =							model(__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       UpperCamelCase_				:   Optional[int]					    =							outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()
       # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
       UpperCamelCase_				:   List[str]					    =							outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       UpperCamelCase_				:   List[Any]					    =							outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=__lowerCAmelCase )

       self.assertIsNotNone(encoder_hidden_states.grad )
       self.assertIsNotNone(pixel_decoder_hidden_states.grad )
       self.assertIsNotNone(transformer_decoder_hidden_states.grad )
       self.assertIsNotNone(attentions.grad )






UpperCamelCase							=1E-4



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





      """simple docstring"""

      UpperCamelCase_				:   int					    =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
      return image




@require_vision
@slow
class     A    (      unittest.TestCase						):






 """simple docstring"""





 @cached_property
 def   _UpperCAmelCase       (							self ):
       return (
           MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           if is_vision_available()
           else None
       )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							self.default_image_processor
       UpperCamelCase_				:   Optional[int]					    =							prepare_img()
       UpperCamelCase_				:   Optional[int]					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   Tuple					    =							model(**__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							torch.tensor(
           [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )

       UpperCamelCase_				:   Any					    =							torch.tensor(
           [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )

       UpperCamelCase_				:   List[Any]					    =							torch.tensor(
           [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   int					    =							self.default_image_processor
       UpperCamelCase_				:   Tuple					    =							prepare_img()
       UpperCamelCase_				:   Optional[Any]					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   List[str]					    =							model(**__lowerCAmelCase )
       # masks_queries_logits
       UpperCamelCase_				:   Any					    =							outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							[
           [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
           [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
           [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
       ]
       UpperCamelCase_				:   Optional[Any]					    =							torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
       # class_queries_logits
       UpperCamelCase_				:   List[str]					    =							outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.tensor(
           [
               [1.6512E00, -5.2572E00, -3.3519E00],
               [3.6169E-02, -5.9025E00, -2.9313E00],
               [1.0766E-04, -7.7630E00, -5.1263E00],
           ] ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   Optional[int]					    =							self.default_image_processor
       UpperCamelCase_				:   Optional[Any]					    =							prepare_img()
       UpperCamelCase_				:   str					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(**__lowerCAmelCase )
       # masks_queries_logits
       UpperCamelCase_				:   Union[str, Any]					    =							outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,     )
       UpperCamelCase_				:   List[str]					    =							[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
       UpperCamelCase_				:   Any					    =							torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
       # class_queries_logits
       UpperCamelCase_				:   List[Any]					    =							outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
       UpperCamelCase_				:   Any					    =							torch.tensor(
           [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   Optional[int]					    =							self.default_image_processor

       UpperCamelCase_				:   Tuple					    =							image_processor(
           [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )]			,     segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )]			,     return_tensors="""pt"""			,     )

       UpperCamelCase_				:   List[Any]					    =							inputs["""pixel_values"""].to(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							[el.to(__lowerCAmelCase ) for el in inputs["""mask_labels"""]]
       UpperCamelCase_				:   Tuple					    =							[el.to(__lowerCAmelCase ) for el in inputs["""class_labels"""]]

       with torch.no_grad():
             UpperCamelCase_				:   Union[str, Any]					    =							model(**__lowerCAmelCase )

       self.assertTrue(outputs.loss is not None )














'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






