




"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 (
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
     )






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		Optional[Any],      a_:		Tuple,      a_:		Any=13,      a_:		Any=7,      a_:		Tuple=True,      a_:		int=True,      a_:		Union[str, Any]=True,      a_:		Optional[int]=True,      a_:		int=99,      a_:		Tuple=64,      a_:		Union[str, Any]=5,      a_:		Optional[int]=4,      a_:		int=37,      a_:		str="gelu",      a_:		Tuple=0.1,      a_:		Any=0.1,      a_:		Tuple=512,      a_:		Optional[Any]=16,      a_:		Union[str, Any]=2,      a_:		int=0.02,      a_:		List[Any]=3,      a_:		List[Any]=4,      a_:		Tuple=None,      ):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  parent
   _snake_case							:		Tuple								=  batch_size
   _snake_case							:		List[Any]								=  seq_length
   _snake_case							:		Union[str, Any]								=  is_training
   _snake_case							:		Union[str, Any]								=  use_input_mask
   _snake_case							:		List[str]								=  use_token_type_ids
   _snake_case							:		Optional[int]								=  use_labels
   _snake_case							:		Optional[Any]								=  vocab_size
   _snake_case							:		str								=  hidden_size
   _snake_case							:		Tuple								=  num_hidden_layers
   _snake_case							:		Optional[Any]								=  num_attention_heads
   _snake_case							:		Optional[Any]								=  intermediate_size
   _snake_case							:		Union[str, Any]								=  hidden_act
   _snake_case							:		Optional[Any]								=  hidden_dropout_prob
   _snake_case							:		Optional[int]								=  attention_probs_dropout_prob
   _snake_case							:		Any								=  max_position_embeddings
   _snake_case							:		Optional[Any]								=  type_vocab_size
   _snake_case							:		Tuple								=  type_sequence_label_size
   _snake_case							:		Optional[int]								=  initializer_range
   _snake_case							:		int								=  num_labels
   _snake_case							:		Dict								=  num_choices
   _snake_case							:		str								=  scope
   _snake_case							:		Optional[int]								=  vocab_size - 1


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size, self.seq_length],      self.vocab_size		)

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

   _snake_case							:		Optional[int]								=  None
   if self.use_labels:
     _snake_case							:		Dict								=  ids_tensor([self.batch_size, self.seq_length],      self.num_labels		)

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

   return config, input_ids, input_mask, token_labels


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   return GPTNeoXConfig(
       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=a_,      initializer_range=self.initializer_range,      pad_token_id=self.pad_token_id,      )


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Dict								=  self.prepare_config_and_inputs()

   _snake_case							:		Optional[Any]								=  True

   return config, input_ids, input_mask, token_labels


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		List[Any],      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  GPTNeoXModel(config=a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Union[str, Any]								=  model(a_,      attention_mask=a_		)
   _snake_case							:		List[Any]								=  model(a_		)
   self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size)		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Union[str, Any],      a_:		Optional[int],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  True
   _snake_case							:		Any								=  GPTNeoXModel(a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Optional[Any]								=  model(a_,      attention_mask=a_		)
   self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size)		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Optional[int],      a_:		int,      a_:		Union[str, Any],      a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  GPTNeoXForCausalLM(config=a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		str								=  model(a_,      attention_mask=a_,      labels=a_		)
   self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size)		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		int,      a_:		List[Any],      a_:		Union[str, Any],      a_:		str		):




   '''simple docstring'''



   _snake_case							:		int								=  self.num_labels
   _snake_case							:		Optional[int]								=  GPTNeoXForQuestionAnswering(a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Tuple								=  model(a_,      attention_mask=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  UpperCamelCase_   (				self:		Dict,      a_:		Dict,      a_:		Optional[int],      a_:		str,      a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.num_labels
   _snake_case							:		Union[str, Any]								=  GPTNeoXForSequenceClassification(a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		List[str]								=  ids_tensor([self.batch_size],      self.type_sequence_label_size		)
   _snake_case							:		List[Any]								=  model(a_,      attention_mask=a_,      labels=a_		)
   self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels)		)


 def  UpperCamelCase_   (				self:		str,      a_:		Optional[Any],      a_:		Tuple,      a_:		List[Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.num_labels
   _snake_case							:		List[str]								=  GPTNeoXForTokenClassification(a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		str								=  model(a_,      attention_mask=a_,      labels=a_		)
   self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels)		)


 def  UpperCamelCase_   (				self:		List[str],      a_:		Dict,      a_:		str,      a_:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  True
   _snake_case							:		Optional[int]								=  GPTNeoXForCausalLM(config=a_		)
   model.to(a_		)
   model.eval()

   # first forward pass
   _snake_case							:		Optional[Any]								=  model(a_,      attention_mask=a_,      use_cache=a_		)
   _snake_case							:		Union[str, Any]								=  outputs.past_key_values

   # create hypothetical multiple next token and extent to next_input_ids
   _snake_case							:		Dict								=  ids_tensor((self.batch_size, 3),      config.vocab_size		)
   _snake_case							:		Optional[Any]								=  ids_tensor((self.batch_size, 3),      vocab_size=2		)

   # append to next input_ids and
   _snake_case							:		Optional[Any]								=  torch.cat([input_ids, next_tokens],      dim=-1		)
   _snake_case							:		List[Any]								=  torch.cat([input_mask, next_mask],      dim=-1		)

   _snake_case							:		int								=  model(a_,      attention_mask=a_,      output_hidden_states=a_		)
   _snake_case							:		str								=  output_from_no_past["""hidden_states"""][0]
   _snake_case							:		Union[str, Any]								=  model(
       a_,      attention_mask=a_,      past_key_values=a_,      output_hidden_states=a_,      )["""hidden_states"""][0]

   # select random slice
   _snake_case							:		Any								=  ids_tensor((1,),      output_from_past.shape[-1]		).item()
   _snake_case							:		str								=  output_from_no_past[:, -3:, random_slice_idx].detach()
   _snake_case							:		int								=  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(a_,      a_,      atol=1E-3		)		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.prepare_config_and_inputs()
   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Dict								=  config_and_inputs
   _snake_case							:		Union[str, Any]								=  {"""input_ids""": input_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class 					lowercase(     __a       ,			__a       ,			__a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					(
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 lowercase__ 					=					(GPTNeoXForCausalLM,) if is_torch_available() else ()
 lowercase__ 					=					(
     {
         "feature-extraction": GPTNeoXModel,
         "question-answering": GPTNeoXForQuestionAnswering,
         "text-classification": GPTNeoXForSequenceClassification,
         "text-generation": GPTNeoXForCausalLM,
         "token-classification": GPTNeoXForTokenClassification,
         "zero-shot": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase__ 					=					False
 lowercase__ 					=					False
 lowercase__ 					=					False
 lowercase__ 					=					False


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  GPTNeoXModelTester(self		)
   _snake_case							:		List[str]								=  ConfigTester(self,      config_class=a_,      hidden_size=64,      num_attention_heads=8		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Dict								=  self.model_tester.prepare_config_and_inputs_for_decoder()

   _snake_case							:		str								=  None

   self.model_tester.create_and_check_model_as_decoder(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Union[str, Any]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_causal_lm(*a_		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*a_		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*a_		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*a_		)


 @unittest.skip(reason="""Feed forward chunking is not implemented"""		)
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   pass


 @parameterized.expand([("""linear""",), ("""dynamic""",)]		)
 def  UpperCamelCase_   (				self:		Optional[int],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Tuple								=  self.model_tester.prepare_config_and_inputs_for_common()
   _snake_case							:		Optional[Any]								=  ids_tensor([1, 10],      config.vocab_size		)
   _snake_case							:		Any								=  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
   _snake_case							:		List[str]								=  GPTNeoXModel(a_		)
   original_model.to(a_		)
   original_model.eval()
   _snake_case							:		Dict								=  original_model(a_		).last_hidden_state
   _snake_case							:		List[Any]								=  original_model(a_		).last_hidden_state

   set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
   _snake_case							:		Optional[int]								=  {"""type""": scaling_type, """factor""": 10.0}
   _snake_case							:		Union[str, Any]								=  GPTNeoXModel(a_		)
   scaled_model.to(a_		)
   scaled_model.eval()
   _snake_case							:		Optional[int]								=  scaled_model(a_		).last_hidden_state
   _snake_case							:		Dict								=  scaled_model(a_		).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(a_,      a_,      atol=1E-5		)		)
   else:
     self.assertFalse(torch.allclose(a_,      a_,      atol=1E-5		)		)

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







@require_torch
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @slow
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)
   for checkpointing in [True, False]:
     _snake_case							:		Dict								=  GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)

     if checkpointing:
       model.gradient_checkpointing_enable()
     else:
       model.gradient_checkpointing_disable()
     model.to(a_		)

     _snake_case							:		Optional[int]								=  tokenizer("""My favorite food is""",      return_tensors="""pt"""		).to(a_		)
     # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
     # See: https://github.com/huggingface/transformers/pull/24193
     _snake_case							:		Tuple								=  """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

     _snake_case							:		str								=  model.generate(**a_,      do_sample=a_,      max_new_tokens=20		)
     _snake_case							:		List[Any]								=  tokenizer.batch_decode(a_		)[0]

     self.assertEqual(a_,      a_		)







'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'conditional_detr'
							lowerCAmelCase       :    List[str]   				=					['past_key_values']
							lowerCAmelCase       :    Optional[int]   				=					{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       List[Any]=300	,_UpperCAmelCase					:       Dict=6	,_UpperCAmelCase					:       List[str]=2048	,_UpperCAmelCase					:       Optional[int]=8	,_UpperCAmelCase					:       List[Any]=6	,_UpperCAmelCase					:       Optional[int]=2048	,_UpperCAmelCase					:       Dict=8	,_UpperCAmelCase					:       int=0.0	,_UpperCAmelCase					:       Optional[Any]=0.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       str="relu"	,_UpperCAmelCase					:       Tuple=256	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       str=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1.0	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int="sine"	,_UpperCAmelCase					:       List[str]="resnet50"	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       str=False	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       int=5	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       Union[str, Any]=1	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Optional[int]=0.25	,**_UpperCAmelCase					:       Tuple	,):
									if backbone_config is not None and use_timm_backbone:
											raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

									if not use_timm_backbone:
											if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
													_a     :      Optional[Any]        =							CONFIG_MAPPING['resnet'](out_features=['stage4']		)
											elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      str        =							backbone_config.get('model_type'		)
													_a     :      Union[str, Any]        =							CONFIG_MAPPING[backbone_model_type]
													_a     :      List[Any]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Tuple        =							use_timm_backbone
									_a     :      Union[str, Any]        =							backbone_config
									_a     :      List[Any]        =							num_channels
									_a     :      Union[str, Any]        =							num_queries
									_a     :      Optional[Any]        =							d_model
									_a     :      Tuple        =							encoder_ffn_dim
									_a     :      Dict        =							encoder_layers
									_a     :      List[str]        =							encoder_attention_heads
									_a     :      Union[str, Any]        =							decoder_ffn_dim
									_a     :      Optional[int]        =							decoder_layers
									_a     :      int        =							decoder_attention_heads
									_a     :      Optional[int]        =							dropout
									_a     :      Tuple        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      str        =							activation_function
									_a     :      Optional[Any]        =							init_std
									_a     :      Union[str, Any]        =							init_xavier_std
									_a     :      List[Any]        =							encoder_layerdrop
									_a     :      List[Any]        =							decoder_layerdrop
									_a     :      Dict        =							encoder_layers
									_a     :      List[Any]        =							auxiliary_loss
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      List[Any]        =							backbone
									_a     :      Optional[int]        =							use_pretrained_backbone
									_a     :      Optional[int]        =							dilation
									# Hungarian matcher
									_a     :      Tuple        =							class_cost
									_a     :      str        =							bbox_cost
									_a     :      Any        =							giou_cost
									# Loss coefficients
									_a     :      Tuple        =							mask_loss_coefficient
									_a     :      Dict        =							dice_loss_coefficient
									_a     :      Tuple        =							cls_loss_coefficient
									_a     :      Any        =							bbox_loss_coefficient
									_a     :      Dict        =							giou_loss_coefficient
									_a     :      Union[str, Any]        =							focal_alpha
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,**_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Dict		):
									return self.encoder_attention_heads







							@property
							def 							__lowercase			(							self					:       str		):
									return self.d_model







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							copy.deepcopy(self.__dict__		)
									if self.backbone_config is not None:
											_a     :      Dict        =							self.backbone_config.to_dict()
									_a     :      Union[str, Any]        =							self.__class__.model_type
									return output

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					version.parse('1.11'   )







							@property
							def 							__lowercase			(							self					:       Dict		):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]		)







							@property
							def 							__lowercase			(							self					:       Any		):
									return 1E-5







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return 12




