




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 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						int							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						List[str]=13							,							_lowerCAmelCase    :						List[str]=7							,							_lowerCAmelCase    :						Optional[int]=True							,							_lowerCAmelCase    :						List[Any]=True							,							_lowerCAmelCase    :						Optional[int]=True							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Optional[int]=99							,							_lowerCAmelCase    :						int=64							,							_lowerCAmelCase    :						str=5							,							_lowerCAmelCase    :						Optional[Any]=4							,							_lowerCAmelCase    :						Tuple=37							,							_lowerCAmelCase    :						Any="gelu"							,							_lowerCAmelCase    :						Optional[Any]=0.1							,							_lowerCAmelCase    :						str=0.1							,							_lowerCAmelCase    :						str=512							,							_lowerCAmelCase    :						List[str]=16							,							_lowerCAmelCase    :						Union[str, Any]=2							,							_lowerCAmelCase    :						Dict=0.02							,							_lowerCAmelCase    :						List[Any]=3							,							_lowerCAmelCase    :						List[str]=4							,							_lowerCAmelCase    :						str=None							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     seq_length
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_input_mask
       SCREAMING_SNAKE_CASE_      		=     use_token_type_ids
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_hidden_layers
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     intermediate_size
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     type_vocab_size
       SCREAMING_SNAKE_CASE_      		=     type_sequence_label_size
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     num_choices
       SCREAMING_SNAKE_CASE_      		=     scope
       SCREAMING_SNAKE_CASE_      		=     vocab_size - 1



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size		)

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

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

       SCREAMING_SNAKE_CASE_      		=     self.get_config()

       return config, input_ids, input_mask, token_labels



 def  lowerCAmelCase_    (				self    :						Tuple		):
       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=_lowerCAmelCase							,							initializer_range=self.initializer_range							,							pad_token_id=self.pad_token_id							,							)



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE_      		=     True

       return config, input_ids, input_mask, token_labels



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)		)



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     True
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXModel(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXForCausalLM(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXForQuestionAnswering(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)
       self.parent.assertEqual(result.start_logits.shape							,							(self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape							,							(self.batch_size, self.seq_length)		)



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXForSequenceClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size]							,							self.type_sequence_label_size		)
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXForTokenClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     True
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXForCausalLM(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()

       # first forward pass
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     outputs.past_key_values

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

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

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     output_from_no_past['hidden_states'][0]
       SCREAMING_SNAKE_CASE_      		=     model(
           _lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							past_key_values=_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase							,							)['hidden_states'][0]

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




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     config_and_inputs
       SCREAMING_SNAKE_CASE_      		=     {'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  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  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase							,							hidden_size=64							,							num_attention_heads=8		)



 def  lowerCAmelCase_    (				self    :						Any		):
       self.config_tester.run_common_tests()



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Dict		):
       # This regression test was failing with PyTorch < 1.3
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_decoder()

       SCREAMING_SNAKE_CASE_      		=     None

       self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase		)



 @unittest.skip(reason='Feed forward chunking is not implemented'		)
 def  lowerCAmelCase_    (				self    :						str		):
       pass




 @parameterized.expand([('linear',), ('dynamic',)]		)
 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([1, 10]							,							config.vocab_size		)
       SCREAMING_SNAKE_CASE_      		=     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
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXModel(_lowerCAmelCase		)
       original_model.to(_lowerCAmelCase		)
       original_model.eval()
       SCREAMING_SNAKE_CASE_      		=     original_model(_lowerCAmelCase		).last_hidden_state
       SCREAMING_SNAKE_CASE_      		=     original_model(_lowerCAmelCase		).last_hidden_state

       set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_      		=     {'type': scaling_type, 'factor': 10.0}
       SCREAMING_SNAKE_CASE_      		=     GPTNeoXModel(_lowerCAmelCase		)
       scaled_model.to(_lowerCAmelCase		)
       scaled_model.eval()
       SCREAMING_SNAKE_CASE_      		=     scaled_model(_lowerCAmelCase		).last_hidden_state
       SCREAMING_SNAKE_CASE_      		=     scaled_model(_lowerCAmelCase		).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(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-5		)		)
       else:
             self.assertFalse(torch.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-5		)		)

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





@require_torch
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 @slow
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'		)
       for checkpointing in [True, False]:
             SCREAMING_SNAKE_CASE_      		=     GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'		)

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

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

             SCREAMING_SNAKE_CASE_      		=     model.generate(**_lowerCAmelCase							,							do_sample=_lowerCAmelCase							,							max_new_tokens=20		)
             SCREAMING_SNAKE_CASE_      		=     tokenizer.batch_decode(_lowerCAmelCase		)[0]

             self.assertEqual(_lowerCAmelCase							,							_lowerCAmelCase		)





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs