





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase		:  Any         =	logging.get_logger(__name__)

UpperCAmelCase		:  List[str]         =	OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

UpperCAmelCase		:  Optional[int]         =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 					_SCREAMING_SNAKE_CASE							(  a    ) -> int:
					for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
										if class_name in extractors:
															__A      :		str         =							model_type_to_module_name(_UpperCAmelCase    )

															__A      :		Dict         =							importlib.import_module(F""".{module_name}"""   ,		'transformers.models'    )
															try:
																				return getattr(_UpperCAmelCase   ,		_UpperCAmelCase    )
															except AttributeError:
																				continue

					for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
										if getattr(_UpperCAmelCase   ,		'__name__'   ,		_UpperCAmelCase    ) == class_name:
															return extractor

															 # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
															 # init and we return the proper dummy to get an appropriate error message.
					__A      :		List[Any]         =							importlib.import_module('transformers'    )
					if hasattr(_UpperCAmelCase   ,		_UpperCAmelCase    ):
										return getattr(_UpperCAmelCase   ,		_UpperCAmelCase    )

					return None







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a = None   ,		a = False   ,		a = False   ,		a = None   ,		a = None   ,		a = None   ,		a = False   ,		**a   ,		) -> Any:
					__A      :		Optional[int]         =							get_file_from_repo(
					    _UpperCAmelCase   ,		_UpperCAmelCase   ,		cache_dir=_UpperCAmelCase   ,		force_download=_UpperCAmelCase   ,		resume_download=_UpperCAmelCase   ,		proxies=_UpperCAmelCase   ,		use_auth_token=_UpperCAmelCase   ,		revision=_UpperCAmelCase   ,		local_files_only=_UpperCAmelCase   ,		)
					if resolved_config_file is None:
										logger.info(
										    'Could not locate the image processor configuration file, will try to use the model config instead.'    )
										return {}

					with open(_UpperCAmelCase   ,		encoding='utf-8'    ) as reader:
										return json.load(_UpperCAmelCase    )





class 		_A:


				"""simple docstring"""


				def __init__(     self	):
									raise EnvironmentError(
									    'AutoImageProcessor is designed to be instantiated '
									    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'	)



				@classmethod
				@replace_list_option_in_docstrings(_snake_case	)
				def    UpperCAmelCase_						(     cls	,   _A	,   **_A	):
									__A      :		Tuple         =							kwargs.pop('config'	,   _snake_case	)
									__A      :		Tuple         =							kwargs.pop('trust_remote_code'	,   _snake_case	)
									__A      :		str         =							True

									__A       ,							__A      :		Optional[Any]         =							ImageProcessingMixin.get_image_processor_dict(_snake_case	,   **_snake_case	)
									__A      :		Optional[int]         =							config_dict.get('image_processor_type'	,   _snake_case	)
									__A      :		Union[str, Any]         =							None
									if "AutoImageProcessor" in config_dict.get('auto_map'	,   {}	):
														__A      :		Dict         =							config_dict['auto_map']['AutoImageProcessor']

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
														__A      :		Optional[int]         =							config_dict.pop('feature_extractor_type'	,   _snake_case	)
														if feature_extractor_class is not None:
																			logger.warning(
																			    'Could not find image processor class in the image processor config or the model config. Loading'
																			    ' based on pattern matching with the model\'s feature extractor configuration.'	)
																			__A      :		List[str]         =							feature_extractor_class.replace('FeatureExtractor'	,   'ImageProcessor'	)
														if "AutoFeatureExtractor" in config_dict.get('auto_map'	,   {}	):
																			__A      :		Tuple         =							config_dict['auto_map']['AutoFeatureExtractor']
																			__A      :		Any         =							feature_extractor_auto_map.replace('FeatureExtractor'	,   'ImageProcessor'	)
																			logger.warning(
																			    'Could not find image processor auto map in the image processor config or the model config.'
																			    ' Loading based on pattern matching with the model\'s feature extractor configuration.'	)

																			    # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
														if not isinstance(_snake_case	,   _snake_case	):
																			__A      :		Union[str, Any]         =							AutoConfig.from_pretrained(_snake_case	,   **_snake_case	)
														# It could be in `config.image_processor_type``
														__A      :		Dict         =							getattr(_snake_case	,   'image_processor_type'	,   _snake_case	)
														if hasattr(_snake_case	,   'auto_map'	) and "AutoImageProcessor" in config.auto_map:
																			__A      :		int         =							config.auto_map['AutoImageProcessor']

									if image_processor_class is not None:
														__A      :		List[str]         =							image_processor_class_from_name(_snake_case	)

									__A      :		Optional[int]         =							image_processor_auto_map is not None
									__A      :		Any         =							image_processor_class is not None or type(_snake_case	) in IMAGE_PROCESSOR_MAPPING
									__A      :		str         =							resolve_trust_remote_code(
									    _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)

									if has_remote_code and trust_remote_code:
														__A      :		Any         =							get_class_from_dynamic_module(
														    _snake_case	,   _snake_case	,   **_snake_case	)
														__A      :		Union[str, Any]         =							kwargs.pop('code_revision'	,   _snake_case	)
														if os.path.isdir(_snake_case	):
																			image_processor_class.register_for_auto_class()
														return image_processor_class.from_dict(_snake_case	,   **_snake_case	)
									elif image_processor_class is not None:
														return image_processor_class.from_dict(_snake_case	,   **_snake_case	)
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(_snake_case	) in IMAGE_PROCESSOR_MAPPING:
														__A      :		Dict         =							IMAGE_PROCESSOR_MAPPING[type(_snake_case	)]
														return image_processor_class.from_dict(_snake_case	,   **_snake_case	)

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()	)}"""	)





				@staticmethod
				def    UpperCAmelCase_						(     _A	,   _A	):
									IMAGE_PROCESSOR_MAPPING.register(_snake_case	,   _snake_case	)











"""simple docstring"""
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=13				,       _snake_case=7				,       _snake_case=True				,       _snake_case=True				,       _snake_case=False				,       _snake_case=True				,       _snake_case=99				,       _snake_case=32				,       _snake_case=5				,       _snake_case=4				,       _snake_case=37				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=16				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=4				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_input_mask
  lowerCAmelCase    				= use_token_type_ids
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= type_sequence_label_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= num_labels
  lowerCAmelCase    				= num_choices
  lowerCAmelCase    				= scope



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )

  lowerCAmelCase    				= None
  if self.use_input_mask:
   lowerCAmelCase    				= random_attention_mask([self.batch_size, self.seq_length]     )

  lowerCAmelCase    				= None
  if self.use_token_type_ids:
   lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size     )

  lowerCAmelCase    				= None
  lowerCAmelCase    				= None
  lowerCAmelCase    				= None
  if self.use_labels:
   lowerCAmelCase    				= ids_tensor([self.batch_size]				,       self.type_sequence_label_size     )
   lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels     )
   lowerCAmelCase    				= ids_tensor([self.batch_size]				,       self.num_choices     )

  lowerCAmelCase    				= self.get_config()

  return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return OpenLlamaConfig(
      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=_snake_case				,       initializer_range=self.initializer_range				,       use_stable_embedding=_snake_case				,       )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= OpenLlamaModel(config=_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case     )
  lowerCAmelCase    				= model(_snake_case     )
  self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= True
  lowerCAmelCase    				= OpenLlamaModel(_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(
      _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       )
  lowerCAmelCase    				= model(
      _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       )
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case     )
  self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= OpenLlamaForCausalLM(config=_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= True
  lowerCAmelCase    				= True
  lowerCAmelCase    				= OpenLlamaForCausalLM(config=_snake_case     )
  model.to(_snake_case     )
  model.eval()

  # first forward pass
  lowerCAmelCase    				= model(
      _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       use_cache=_snake_case				,       )
  lowerCAmelCase    				= outputs.past_key_values

  # create hypothetical multiple next token and extent to next_input_ids
  lowerCAmelCase    				= ids_tensor((self.batch_size, 3)				,       config.vocab_size     )
  lowerCAmelCase    				= ids_tensor((self.batch_size, 3)				,       vocab_size=2     )

  # append to next input_ids and
  lowerCAmelCase    				= torch.cat([input_ids, next_tokens]				,       dim=-1     )
  lowerCAmelCase    				= torch.cat([input_mask, next_mask]				,       dim=-1     )

  lowerCAmelCase    				= model(
      _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       output_hidden_states=_snake_case				,       )['hidden_states'][0]
  lowerCAmelCase    				= model(
      _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       past_key_values=_snake_case				,       output_hidden_states=_snake_case				,       )['hidden_states'][0]

  # select random slice
  lowerCAmelCase    				= ids_tensor((1,)				,       output_from_past.shape[-1]     ).item()
  lowerCAmelCase    				= output_from_no_past[:, -3:, random_slice_idx].detach()
  lowerCAmelCase    				= output_from_past[:, :, random_slice_idx].detach()

  self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

  # test that outputs are equal for slice
  self.parent.assertTrue(torch.allclose(_snake_case				,       _snake_case				,       atol=1E-3     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,
  )    				= config_and_inputs
  lowerCAmelCase    				= {'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict







@require_torch
class       a							(   a__   , a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 snake_case__             =       (OpenLlamaForCausalLM,) if is_torch_available() else ()
 snake_case__             =       (
     {
         '''feature-extraction''': OpenLlamaModel,
         '''text-classification''': OpenLlamaForSequenceClassification,
         '''text-generation''': OpenLlamaForCausalLM,
         '''zero-shot''': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= OpenLlamaModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   lowerCAmelCase    				= type
   self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase    				= 3
  lowerCAmelCase    				= input_dict['input_ids']
  lowerCAmelCase    				= input_ids.ne(1     ).to(_snake_case     )
  lowerCAmelCase    				= ids_tensor([self.model_tester.batch_size]				,       self.model_tester.type_sequence_label_size     )
  lowerCAmelCase    				= OpenLlamaForSequenceClassification(_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case     )
  self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase    				= 3
  lowerCAmelCase    				= 'single_label_classification'
  lowerCAmelCase    				= input_dict['input_ids']
  lowerCAmelCase    				= input_ids.ne(1     ).to(_snake_case     )
  lowerCAmelCase    				= ids_tensor([self.model_tester.batch_size]				,       self.model_tester.type_sequence_label_size     )
  lowerCAmelCase    				= OpenLlamaForSequenceClassification(_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case     )
  self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase    				= 3
  lowerCAmelCase    				= 'multi_label_classification'
  lowerCAmelCase    				= input_dict['input_ids']
  lowerCAmelCase    				= input_ids.ne(1     ).to(_snake_case     )
  lowerCAmelCase    				= ids_tensor(
      [self.model_tester.batch_size, config.num_labels]				,       self.model_tester.type_sequence_label_size     ).to(torch.float     )
  lowerCAmelCase    				= OpenLlamaForSequenceClassification(_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case     )
  self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)     )



 @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass



 @parameterized.expand([('linear',), ('dynamic',)]     )
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase    				= ids_tensor([1, 10]				,       config.vocab_size     )
  lowerCAmelCase    				= ids_tensor([1, int(config.max_position_embeddings * 1.5     )]				,       config.vocab_size     )

  set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
  lowerCAmelCase    				= OpenLlamaModel(_snake_case     )
  original_model.to(_snake_case     )
  original_model.eval()
  lowerCAmelCase    				= original_model(_snake_case     ).last_hidden_state
  lowerCAmelCase    				= original_model(_snake_case     ).last_hidden_state

  set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
  lowerCAmelCase    				= {'type': scaling_type, 'factor': 10.0}
  lowerCAmelCase    				= OpenLlamaModel(_snake_case     )
  scaled_model.to(_snake_case     )
  scaled_model.eval()
  lowerCAmelCase    				= scaled_model(_snake_case     ).last_hidden_state
  lowerCAmelCase    				= scaled_model(_snake_case     ).last_hidden_state

  # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
  # maximum sequence length, so the outputs for the short input should match.
  if scaling_type == "dynamic":
   self.assertTrue(torch.allclose(_snake_case				,       _snake_case				,       atol=1E-5     )     )
  else:
   self.assertFalse(torch.allclose(_snake_case				,       _snake_case				,       atol=1E-5     )     )

  # The output should be different for long inputs
  self.assertFalse(torch.allclose(_snake_case				,       _snake_case				,       atol=1E-5     )     )





