





'''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       a_   :





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





        """simple docstring"""




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





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





        """simple docstring"""




        lowercase_			:List[str]    			= ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

        lowercase_			:Union[str, Any]    			= None
        if self.use_input_mask:
           lowercase_			:Dict    			= random_attention_mask([self.batch_size, self.seq_length]						)

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

        lowercase_			:int    			= self.get_config()

        return config, input_ids, input_mask, token_labels





     def 							lowercase__      ( self		:					Optional[int]						):





        """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=lowercase	,      initializer_range=self.initializer_range	,      pad_token_id=self.pad_token_id	,      )





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:str    			= self.prepare_config_and_inputs()

        lowercase_			:str    			= True

        return config, input_ids, input_mask, token_labels





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					List[Any]	,      lowercase		:					Any	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Tuple    			= GPTNeoXModel(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_			:Optional[int]    			= model(lowercase	,      attention_mask=lowercase						)
        lowercase_			:Optional[int]    			= model(lowercase						)
        self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Dict	,      lowercase		:					Union[str, Any]	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= True
        lowercase_			:Any    			= GPTNeoXModel(lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_			:Optional[Any]    			= model(lowercase	,      attention_mask=lowercase						)
        self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)





     def 							lowercase__      ( self		:					Any	,      lowercase		:					Optional[Any]	,      lowercase		:					Tuple	,      lowercase		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= GPTNeoXForCausalLM(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_			:List[str]    			= model(lowercase	,      attention_mask=lowercase	,      labels=lowercase						)
        self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					List[str]	,      lowercase		:					List[str]	,      lowercase		:					Optional[Any]	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:str    			= self.num_labels
        lowercase_			:Tuple    			= GPTNeoXForQuestionAnswering(lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_			:List[Any]    			= model(lowercase	,      attention_mask=lowercase						)
        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 							lowercase__      ( self		:					Dict	,      lowercase		:					int	,      lowercase		:					Tuple	,      lowercase		:					List[str]	,      lowercase		:					Any						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Dict	,      lowercase		:					List[Any]	,      lowercase		:					Any	,      lowercase		:					int						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Dict	,      lowercase		:					Dict	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:Tuple    			= True
        lowercase_			:List[Any]    			= GPTNeoXForCausalLM(config=lowercase						)
        model.to(lowercase						)
        model.eval()

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

        # create hypothetical multiple next token and extent to next_input_ids
        lowercase_			:Union[str, Any]    			= ids_tensor((self.batch_size, 3)	,      config.vocab_size						)
        lowercase_			:int    			= ids_tensor((self.batch_size, 3)	,      vocab_size=2						)

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

        lowercase_			:str    			= model(lowercase	,      attention_mask=lowercase	,      output_hidden_states=lowercase						)
        lowercase_			:Optional[int]    			= output_from_no_past["hidden_states"][0]
        lowercase_			:Union[str, Any]    			= model(
            lowercase	,      attention_mask=lowercase	,      past_key_values=lowercase	,      output_hidden_states=lowercase	,      )["hidden_states"][0]

        # select random slice
        lowercase_			:Optional[int]    			= ids_tensor((1,)	,      output_from_past.shape[-1]						).item()
        lowercase_			:Tuple    			= output_from_no_past[:, -3:, random_slice_idx].detach()
        lowercase_			:Optional[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(lowercase	,      lowercase	,      atol=1e-3						)						)







     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Any    			= config_and_inputs
        lowercase_			:int    			= {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict







@require_torch
class       a_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			(
         (
             GPTNeoXModel,
             GPTNeoXForCausalLM,
             GPTNeoXForQuestionAnswering,
             GPTNeoXForSequenceClassification,
             GPTNeoXForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __A      =			(GPTNeoXForCausalLM,) if is_torch_available() else ()
     __A      =			(
         {
             "feature-extraction": GPTNeoXModel,
             "question-answering": GPTNeoXForQuestionAnswering,
             "text-classification": GPTNeoXForSequenceClassification,
             "text-generation": GPTNeoXForCausalLM,
             "token-classification": GPTNeoXForTokenClassification,
             "zero-shot": GPTNeoXForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __A      =			False
     __A      =			False
     __A      =			False
     __A      =			False





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= GPTNeoXModelTester(self						)
        lowercase_			:str    			= ConfigTester(self	,      config_class=lowercase	,      hidden_size=64	,      num_attention_heads=8						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Optional[int]    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(lowercase	,      lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:List[Any]    			= self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(lowercase	,      lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Any    			= self.model_tester.prepare_config_and_inputs_for_decoder()

        lowercase_			:Optional[Any]    			= None

        self.model_tester.create_and_check_model_as_decoder(lowercase	,      lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Optional[int]    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase	,      lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Tuple    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*lowercase						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Dict    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:str    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase						)





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase						)





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





        """simple docstring"""




        pass







     @parameterized.expand([("linear",), ("dynamic",)]						)
     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Optional[Any]						):





        """simple docstring"""




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

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

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







@require_torch
class       a_   (				unittest.TestCase							):





     @slow
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"						)
        for checkpointing in [True, False]:
           lowercase_			:Optional[Any]    			= GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"						)

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

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

           lowercase_			:Optional[int]    			= model.generate(**lowercase	,      do_sample=lowercase	,      max_new_tokens=20						)
           lowercase_			:List[Any]    			= tokenizer.batch_decode(lowercase						)[0]

           self.assertEqual(lowercase	,      lowercase						)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:     Optional[Any]												=logging.get_logger(__name__)

lowerCAmelCase					:     Tuple												={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"vit_mae"





     def __init__( self		:					Any	,      lowercase		:					int=768	,      lowercase		:					Tuple=12	,      lowercase		:					str=12	,      lowercase		:					Optional[Any]=3_072	,      lowercase		:					List[Any]="gelu"	,      lowercase		:					Tuple=0.0	,      lowercase		:					Union[str, Any]=0.0	,      lowercase		:					str=0.02	,      lowercase		:					Optional[int]=1e-1_2	,      lowercase		:					List[Any]=224	,      lowercase		:					str=16	,      lowercase		:					List[str]=3	,      lowercase		:					Optional[Any]=True	,      lowercase		:					int=16	,      lowercase		:					Optional[Any]=512	,      lowercase		:					Optional[Any]=8	,      lowercase		:					Optional[Any]=2_048	,      lowercase		:					List[str]=0.75	,      lowercase		:					str=False	,      **lowercase		:					Union[str, Any]	,      ):





        """simple docstring"""




        super().__init__(**lowercase						)

        lowercase_			:Any    			= hidden_size
        lowercase_			:Optional[Any]    			= num_hidden_layers
        lowercase_			:Optional[Any]    			= num_attention_heads
        lowercase_			:int    			= intermediate_size
        lowercase_			:Optional[int]    			= hidden_act
        lowercase_			:str    			= hidden_dropout_prob
        lowercase_			:Optional[Any]    			= attention_probs_dropout_prob
        lowercase_			:str    			= initializer_range
        lowercase_			:Optional[int]    			= layer_norm_eps
        lowercase_			:str    			= image_size
        lowercase_			:Union[str, Any]    			= patch_size
        lowercase_			:Dict    			= num_channels
        lowercase_			:Any    			= qkv_bias
        lowercase_			:Optional[int]    			= decoder_num_attention_heads
        lowercase_			:Optional[Any]    			= decoder_hidden_size
        lowercase_			:Union[str, Any]    			= decoder_num_hidden_layers
        lowercase_			:List[Any]    			= decoder_intermediate_size
        lowercase_			:Optional[Any]    			= mask_ratio
        lowercase_			:Optional[Any]    			= norm_pix_loss


