





"""simple docstring"""






import argparse
import json
from pathlib import Path

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

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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

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






       """simple docstring"""




       if "backbone.0.body" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('backbone.0.body','backbone.conv_encoder.model'				)
       if "transformer" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('transformer.',''				)
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     _UpperCAmelCase			     =  orig_key.replace('norm1','self_attn_layer_norm'				)
              else:
                     _UpperCAmelCase			     =  orig_key.replace('norm1','encoder_attn_layer_norm'				)
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     _UpperCAmelCase			     =  orig_key.replace('norm2','final_layer_norm'				)
              else:
                     _UpperCAmelCase			     =  orig_key.replace('norm2','self_attn_layer_norm'				)
       if "norm3" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('norm3','final_layer_norm'				)
       if "linear1" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('linear1','fc1'				)
       if "linear2" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('linear2','fc2'				)
       if "query_embed" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('query_embed','query_position_embeddings'				)
       if "cross_attn" in orig_key:
              _UpperCAmelCase			     =  orig_key.replace('cross_attn','encoder_attn'				)

       return orig_key

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       for i in range(6				):
              # read in weights + bias of input projection layer of self-attention
              _UpperCAmelCase			     =  state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
              _UpperCAmelCase			     =  state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""				)
              # next, add query, keys and values (in that order) to the state dict
              _UpperCAmelCase			     =  in_proj_weight[:256, :]
              _UpperCAmelCase			     =  in_proj_bias[:256]
              _UpperCAmelCase			     =  in_proj_weight[256:512, :]
              _UpperCAmelCase			     =  in_proj_bias[256:512]
              _UpperCAmelCase			     =  in_proj_weight[-256:, :]
              _UpperCAmelCase			     =  in_proj_bias[-256:]

def    __lowerCamelCase     (							)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
       _UpperCAmelCase			     =  Image.open(requests.get(SCREAMING_SNAKE_CASE,stream=SCREAMING_SNAKE_CASE				).raw				)

       return im

@torch.no_grad()
def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  DeformableDetrConfig()
       # set config attributes
       if single_scale:
              _UpperCAmelCase			     =  1
       _UpperCAmelCase			     =  dilation
       _UpperCAmelCase			     =  with_box_refine
       _UpperCAmelCase			     =  two_stage
       # set labels
       _UpperCAmelCase			     =  91
       _UpperCAmelCase			     =  'huggingface/label-files'
       _UpperCAmelCase			     =  'coco-detection-id2label.json'
       _UpperCAmelCase			     =  json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,repo_type='dataset'				)				),'r'				)				)
       _UpperCAmelCase			     =  {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
       _UpperCAmelCase			     =  idalabel
       _UpperCAmelCase			     =  {v: k for k, v in idalabel.items()}

       # load image processor
       _UpperCAmelCase			     =  DeformableDetrImageProcessor(format='coco_detection'				)

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

       logger.info('Converting model...'				)

       # load original state dict
       _UpperCAmelCase			     =  torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'				)['model']
       # rename keys
       for key in state_dict.copy().keys():
              _UpperCAmelCase			     =  state_dict.pop(SCREAMING_SNAKE_CASE				)
              _UpperCAmelCase			     =  val
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE				)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       _UpperCAmelCase			     =  'model.'
       for key in state_dict.copy().keys():
              if not key.startswith('class_embed'				) and not key.startswith('bbox_embed'				):
                     _UpperCAmelCase			     =  state_dict.pop(SCREAMING_SNAKE_CASE				)
                     _UpperCAmelCase			     =  val
    # finally, create HuggingFace model and load state dict
       _UpperCAmelCase			     =  DeformableDetrForObjectDetection(SCREAMING_SNAKE_CASE				)
       model.load_state_dict(SCREAMING_SNAKE_CASE				)
       model.eval()

       _UpperCAmelCase			     =  'cuda' if torch.cuda.is_available() else 'cpu'
       model.to(SCREAMING_SNAKE_CASE				)
       # verify our conversion
       _UpperCAmelCase			     =  model(pixel_values.to(SCREAMING_SNAKE_CASE				)				)

       _UpperCAmelCase			     =  torch.tensor(
           [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]				)
       _UpperCAmelCase			     =  torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]				)

       if single_scale:
              _UpperCAmelCase			     =  torch.tensor(
                  [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]				)
              _UpperCAmelCase			     =  torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]				)

       if single_scale and dilation:
              _UpperCAmelCase			     =  torch.tensor(
                  [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]				)
              _UpperCAmelCase			     =  torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]				)

       if with_box_refine:
              _UpperCAmelCase			     =  torch.tensor(
                  [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]				)
              _UpperCAmelCase			     =  torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]				)

       if with_box_refine and two_stage:
              _UpperCAmelCase			     =  torch.tensor(
                  [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]				)
              _UpperCAmelCase			     =  torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]				)

       print('Logits:',outputs.logits[0, :3, :3]				)

       assert torch.allclose(outputs.logits[0, :3, :3],expected_logits.to(SCREAMING_SNAKE_CASE				),atol=1E-4				)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3],expected_boxes.to(SCREAMING_SNAKE_CASE				),atol=1E-4				)

       print('Everything ok!'				)

       # Save model and image processor
       logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
       Path(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
       model.save_pretrained(SCREAMING_SNAKE_CASE				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)

       # Push to hub
       if push_to_hub:
              _UpperCAmelCase			     =  'deformable-detr'
              model_name += "-single-scale" if single_scale else ""
              model_name += "-dc5" if dilation else ""
              model_name += "-with-box-refine" if with_box_refine else ""
              model_name += "-two-stage" if two_stage else ""
              print('Pushing model to hub...'				)
              model.push_to_hub(repo_path_or_name=SCREAMING_SNAKE_CASE,organization='nielsr',commit_message='Add model'				)


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

    parser.add_argument(
        '''--checkpoint_path''',
        type=str,
        default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
        help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
    )
    parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
    parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
    parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
    parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default=None,
        type=str,
        required=True,
        help='''Path to the folder to output PyTorch model.''',
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )
    lowerCAmelCase_      =				parser.parse_args()
    convert_deformable_detr_checkpoint(
        args.checkpoint_path,
        args.single_scale,
        args.dilation,
        args.with_box_refine,
        args.two_stage,
        args.pytorch_dump_folder_path,
        args.push_to_hub,
    )













"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMModel,
        TFXLMWithLMHeadModel,
        XLMConfig,
    )



class 						lowerCAmelCase       :






       def __init__( self     ,	a__     ,	):
              _UpperCAmelCase			     =  parent
              _UpperCAmelCase			     =  13
              _UpperCAmelCase			     =  7
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  False
              _UpperCAmelCase			     =  False
              _UpperCAmelCase			     =  False
              _UpperCAmelCase			     =  2
              _UpperCAmelCase			     =  99
              _UpperCAmelCase			     =  0
              _UpperCAmelCase			     =  32
              _UpperCAmelCase			     =  2
              _UpperCAmelCase			     =  4
              _UpperCAmelCase			     =  0.1
              _UpperCAmelCase			     =  0.1
              _UpperCAmelCase			     =  5_12
              _UpperCAmelCase			     =  16
              _UpperCAmelCase			     =  2
              _UpperCAmelCase			     =  0.02
              _UpperCAmelCase			     =  3
              _UpperCAmelCase			     =  4
              _UpperCAmelCase			     =  'last'
              _UpperCAmelCase			     =  True
              _UpperCAmelCase			     =  None
              _UpperCAmelCase			     =  0






       def 				__A			( self  ):
              _UpperCAmelCase			     =  ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size  )
              _UpperCAmelCase			     =  random_attention_mask([self.batch_size, self.seq_length]     ,	dtype=tf.floataa  )

              _UpperCAmelCase			     =  None
              if self.use_input_lengths:
                     _UpperCAmelCase			     =  (
                         ids_tensor([self.batch_size]     ,	vocab_size=2  ) + self.seq_length - 2
                     )  # small variation of seq_length

              _UpperCAmelCase			     =  None
              if self.use_token_type_ids:
                     _UpperCAmelCase			     =  ids_tensor([self.batch_size, self.seq_length]     ,	self.n_langs  )

              _UpperCAmelCase			     =  None
              _UpperCAmelCase			     =  None
              _UpperCAmelCase			     =  None
              if self.use_labels:
                     _UpperCAmelCase			     =  ids_tensor([self.batch_size]     ,	self.type_sequence_label_size  )
                     _UpperCAmelCase			     =  ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels  )
                     _UpperCAmelCase			     =  ids_tensor([self.batch_size]     ,	2     ,	dtype=tf.floataa  )
                     _UpperCAmelCase			     =  ids_tensor([self.batch_size]     ,	self.num_choices  )

              _UpperCAmelCase			     =  XLMConfig(
                  vocab_size=self.vocab_size     ,	n_special=self.n_special     ,	emb_dim=self.hidden_size     ,	n_layers=self.num_hidden_layers     ,	n_heads=self.num_attention_heads     ,	dropout=self.hidden_dropout_prob     ,	attention_dropout=self.attention_probs_dropout_prob     ,	gelu_activation=self.gelu_activation     ,	sinusoidal_embeddings=self.sinusoidal_embeddings     ,	asm=self.asm     ,	causal=self.causal     ,	n_langs=self.n_langs     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	summary_type=self.summary_type     ,	use_proj=self.use_proj     ,	bos_token_id=self.bos_token_id     ,	)

              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_lengths,
                  sequence_labels,
                  token_labels,
                  is_impossible_labels,
                  choice_labels,
                  input_mask,
              )






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  TFXLMModel(config=a__  )
              _UpperCAmelCase			     =  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
              _UpperCAmelCase			     =  model(a__  )

              _UpperCAmelCase			     =  [input_ids, input_mask]
              _UpperCAmelCase			     =  model(a__  )
              self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  TFXLMWithLMHeadModel(a__  )

              _UpperCAmelCase			     =  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
              _UpperCAmelCase			     =  model(a__  )

              _UpperCAmelCase			     =  outputs

              self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  TFXLMForQuestionAnsweringSimple(a__  )

              _UpperCAmelCase			     =  {'input_ids': input_ids, 'lengths': input_lengths}

              _UpperCAmelCase			     =  model(a__  )

              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 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  TFXLMForSequenceClassification(a__  )

              _UpperCAmelCase			     =  {'input_ids': input_ids, 'lengths': input_lengths}

              _UpperCAmelCase			     =  model(a__  )

              self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  self.num_labels
              _UpperCAmelCase			     =  TFXLMForTokenClassification(config=a__  )
              _UpperCAmelCase			     =  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
              _UpperCAmelCase			     =  model(a__  )
              self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              _UpperCAmelCase			     =  self.num_choices
              _UpperCAmelCase			     =  TFXLMForMultipleChoice(config=a__  )
              _UpperCAmelCase			     =  tf.tile(tf.expand_dims(a__     ,	1  )     ,	(1, self.num_choices, 1)  )
              _UpperCAmelCase			     =  tf.tile(tf.expand_dims(a__     ,	1  )     ,	(1, self.num_choices, 1)  )
              _UpperCAmelCase			     =  tf.tile(tf.expand_dims(a__     ,	1  )     ,	(1, self.num_choices, 1)  )
              _UpperCAmelCase			     =  {
                  'input_ids': multiple_choice_inputs_ids,
                  'attention_mask': multiple_choice_input_mask,
                  'token_type_ids': multiple_choice_token_type_ids,
              }
              _UpperCAmelCase			     =  model(a__  )
              self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.prepare_config_and_inputs()
              (
                  (
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				(
                  _UpperCAmelCase
              )     ,				
              )			     =  config_and_inputs
              _UpperCAmelCase			     =  {
                  'input_ids': input_ids,
                  'token_type_ids': token_type_ids,
                  'langs': token_type_ids,
                  'lengths': input_lengths,
              }
              return config, inputs_dict



@require_tf
class 						lowerCAmelCase       (   snake_case      ,						snake_case      ,						unittest.TestCase				):
       lowerCAmelCase__    			=	(
           (
               TFXLMModel,
               TFXLMWithLMHeadModel,
               TFXLMForSequenceClassification,
               TFXLMForQuestionAnsweringSimple,
               TFXLMForTokenClassification,
               TFXLMForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       lowerCAmelCase__    			=	(
           (TFXLMWithLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       lowerCAmelCase__    			=	(
           {
               """feature-extraction""": TFXLMModel,
               """fill-mask""": TFXLMWithLMHeadModel,
               """question-answering""": TFXLMForQuestionAnsweringSimple,
               """text-classification""": TFXLMForSequenceClassification,
               """text-generation""": TFXLMWithLMHeadModel,
               """token-classification""": TFXLMForTokenClassification,
               """zero-shot""": TFXLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase__    			=	False
       lowerCAmelCase__    			=	False






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__  ):
              if (
                  pipeline_test_casse_name == "QAPipelineTests"
                  and tokenizer_name is not None
                  and not tokenizer_name.endswith('Fast'  )
              ):
                     # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                     # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                     # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                     return True

              return False






       def 				__A			( self  ):
              _UpperCAmelCase			     =  TFXLMModelTester(self  )
              _UpperCAmelCase			     =  ConfigTester(self     ,	config_class=a__     ,	emb_dim=37  )






       def 				__A			( self  ):
              self.config_tester.run_common_tests()






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_model(*a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_lm_head(*a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_qa(*a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_sequence_classif(*a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_for_token_classification(*a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_for_multiple_choice(*a__  )






       @slow
       def 				__A			( self  ):
              for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase			     =  TFXLMModel.from_pretrained(a__  )
                     self.assertIsNotNone(a__  )



@require_tf
class 						lowerCAmelCase       (   unittest.TestCase				):






       @slow
       def 				__A			( self  ):
              _UpperCAmelCase			     =  TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'  )
              _UpperCAmelCase			     =  tf.convert_to_tensor([[14, 4_47]]     ,	dtype=tf.intaa  )  # the president
              _UpperCAmelCase			     =  [
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
                  14,
                  4_47,
              ]  # the president the president the president the president the president the president the president the president the president the president
              # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
              _UpperCAmelCase			     =  model.generate(a__     ,	do_sample=a__  )
              self.assertListEqual(output_ids[0].numpy().tolist()     ,	a__  )






