





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        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Any		, A					:     int		, A					:     Tuple=1_3		, A					:     Tuple=7		, A					:     int=True		, A					:     Optional[Any]=True		, A					:     Dict=True		, A					:     List[Any]=True		, A					:     List[str]=9_9		, A					:     List[str]=6_4		, A					:     int=5		, A					:     Optional[int]=4		, A					:     Any=3_7		, A					:     Optional[int]="gelu"		, A					:     Dict=0.1		, A					:     int=0.1		, A					:     str=5_1_2		, A					:     List[str]=1_6		, A					:     Optional[Any]=2		, A					:     Optional[Any]=0.02		, A					:     Tuple=3		, A					:     List[Any]=4		, A					:     int=None		, )       ->Optional[Any]:
          lowerCamelCase__       :						str        =							parent
          lowerCamelCase__       :						str        =							batch_size
          lowerCamelCase__       :						Optional[Any]        =							seq_length
          lowerCamelCase__       :						Union[str, Any]        =							is_training
          lowerCamelCase__       :						Any        =							use_input_mask
          lowerCamelCase__       :						int        =							use_token_type_ids
          lowerCamelCase__       :						int        =							use_labels
          lowerCamelCase__       :						List[str]        =							vocab_size
          lowerCamelCase__       :						Union[str, Any]        =							hidden_size
          lowerCamelCase__       :						str        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Any        =							intermediate_size
          lowerCamelCase__       :						Dict        =							hidden_act
          lowerCamelCase__       :						Optional[int]        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						Tuple        =							max_position_embeddings
          lowerCamelCase__       :						List[str]        =							type_vocab_size
          lowerCamelCase__       :						int        =							type_sequence_label_size
          lowerCamelCase__       :						Optional[Any]        =							initializer_range
          lowerCamelCase__       :						int        =							num_labels
          lowerCamelCase__       :						str        =							num_choices
          lowerCamelCase__       :						str        =							scope
          lowerCamelCase__       :						Union[str, Any]        =							vocab_size - 1







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Any:
          lowerCamelCase__       :						Dict        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						int        =							None
          if self.use_input_mask:
             lowerCamelCase__       :						str        =							random_attention_mask([self.batch_size, self.seq_length]      )

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

          lowerCamelCase__       :						Tuple        =							self.get_config()

          return config, input_ids, input_mask, token_labels







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->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=A		, initializer_range=self.initializer_range		, pad_token_id=self.pad_token_id		, )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->int:
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							self.prepare_config_and_inputs()

          lowerCamelCase__       :						int        =							True

          return config, input_ids, input_mask, token_labels







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     Union[str, Any]		, A					:     List[str]		, A					:     Tuple      )       ->List[Any]:
          lowerCamelCase__       :						List[str]        =							GPTNeoXModel(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Union[str, Any]        =							model(A		, attention_mask=A      )
          lowerCamelCase__       :						Tuple        =							model(A      )
          self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Dict		, A					:     List[str]		, A					:     Tuple      )       ->List[Any]:
          lowerCamelCase__       :						int        =							True
          lowerCamelCase__       :						Union[str, Any]        =							GPTNeoXModel(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						List[Any]        =							model(A		, attention_mask=A      )
          self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Optional[int]		, A					:     int		, A					:     Optional[Any]		, A					:     Any      )       ->str:
          lowerCamelCase__       :						Tuple        =							GPTNeoXForCausalLM(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						List[str]        =							model(A		, attention_mask=A		, labels=A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)      )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     int		, A					:     int		, A					:     int		, A					:     List[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							self.num_labels
          lowerCamelCase__       :						Tuple        =							GPTNeoXForQuestionAnswering(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Any        =							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 						__lowerCamelCase   (				self					:     Any		, A					:     int		, A					:     Optional[Any]		, A					:     Dict		, A					:     Dict      )       ->int:
          lowerCamelCase__       :						int        =							self.num_labels
          lowerCamelCase__       :						Any        =							GPTNeoXForSequenceClassification(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Optional[Any]        =							ids_tensor([self.batch_size]		, self.type_sequence_label_size      )
          lowerCamelCase__       :						Optional[Any]        =							model(A		, attention_mask=A		, labels=A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     str		, A					:     Tuple		, A					:     Tuple		, A					:     List[Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						str        =							self.num_labels
          lowerCamelCase__       :						List[Any]        =							GPTNeoXForTokenClassification(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Tuple        =							model(A		, attention_mask=A		, labels=A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     List[str]		, A					:     str		, A					:     Tuple      )       ->Union[str, Any]:
          lowerCamelCase__       :						str        =							True
          lowerCamelCase__       :						List[str]        =							GPTNeoXForCausalLM(config=A      )
          model.to(A      )
          model.eval()

          # first forward pass
          lowerCamelCase__       :						int        =							model(A		, attention_mask=A		, use_cache=A      )
          lowerCamelCase__       :						Dict        =							outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          lowerCamelCase__       :						List[str]        =							ids_tensor((self.batch_size, 3)		, config.vocab_size      )
          lowerCamelCase__       :						Tuple        =							ids_tensor((self.batch_size, 3)		, vocab_size=2      )

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

          lowerCamelCase__       :						Union[str, Any]        =							model(A		, attention_mask=A		, output_hidden_states=A      )
          lowerCamelCase__       :						List[Any]        =							output_from_no_past['''hidden_states'''][0]
          lowerCamelCase__       :						Dict        =							model(
              A		, attention_mask=A		, past_key_values=A		, output_hidden_states=A		, )['''hidden_states'''][0]

          # select random slice
          lowerCamelCase__       :						List[Any]        =							ids_tensor((1,)		, output_from_past.shape[-1]      ).item()
          lowerCamelCase__       :						Dict        =							output_from_no_past[:, -3:, random_slice_idx].detach()
          lowerCamelCase__       :						Optional[Any]        =							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 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Dict        =							self.prepare_config_and_inputs()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							config_and_inputs
          lowerCamelCase__       :						Optional[Any]        =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[int]			   =   (
           (
               GPTNeoXModel,
               GPTNeoXForCausalLM,
               GPTNeoXForQuestionAnswering,
               GPTNeoXForSequenceClassification,
               GPTNeoXForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       _UpperCAmelCase				:  int			   =   (GPTNeoXForCausalLM,) if is_torch_available() else ()
       _UpperCAmelCase				:  Tuple			   =   (
           {
               "feature-extraction": GPTNeoXModel,
               "question-answering": GPTNeoXForQuestionAnswering,
               "text-classification": GPTNeoXForSequenceClassification,
               "text-generation": GPTNeoXForCausalLM,
               "token-classification": GPTNeoXForTokenClassification,
               "zero-shot": GPTNeoXForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  Dict			   =   False
       _UpperCAmelCase				:  Optional[Any]			   =   False
       _UpperCAmelCase				:  Optional[Any]			   =   False







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						List[Any]        =							GPTNeoXModelTester(self      )
          lowerCamelCase__       :						Optional[int]        =							ConfigTester(self		, config_class=A		, hidden_size=6_4		, num_attention_heads=8      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Any:
          # This regression test was failing with PyTorch < 1.3
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_decoder()

          lowerCamelCase__       :						str        =							None

          self.model_tester.create_and_check_model_as_decoder(A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          lowerCamelCase__       :						List[str]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							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 						__lowerCamelCase   (				self					:     Tuple      )       ->int:
          pass







       @parameterized.expand([('''linear''',), ('''dynamic''',)]      )
       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Any      )       ->Any:
          lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						int        =							ids_tensor([1, 1_0]		, config.vocab_size      )
          lowerCamelCase__       :						Any        =							ids_tensor([1, int(config.max_position_embeddings * 1.5      )]		, config.vocab_size      )

          set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
          lowerCamelCase__       :						List[str]        =							GPTNeoXModel(A      )
          original_model.to(A      )
          original_model.eval()
          lowerCamelCase__       :						Union[str, Any]        =							original_model(A      ).last_hidden_state
          lowerCamelCase__       :						Optional[Any]        =							original_model(A      ).last_hidden_state

          set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
          lowerCamelCase__       :						List[Any]        =							{'''type''': scaling_type, '''factor''': 10.0}
          lowerCamelCase__       :						List[str]        =							GPTNeoXModel(A      )
          scaled_model.to(A      )
          scaled_model.eval()
          lowerCamelCase__       :						Optional[Any]        =							scaled_model(A      ).last_hidden_state
          lowerCamelCase__       :						Union[str, Any]        =							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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @slow
       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						int        =							AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )
          for checkpointing in [True, False]:
             lowerCamelCase__       :						str        =							GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )

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

             lowerCamelCase__       :						Union[str, Any]        =							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
             lowerCamelCase__       :						Any        =							'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

             lowerCamelCase__       :						Optional[Any]        =							model.generate(**A		, do_sample=A		, max_new_tokens=2_0      )
             lowerCamelCase__       :						Optional[Any]        =							tokenizer.batch_decode(A      )[0]

             self.assertEqual(A		, A      )










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def 	_a						(   *UpperCAmelCase      ,  UpperCAmelCase = None      ,  UpperCAmelCase=True      ,  UpperCAmelCase=2					)    ->    str:







   """simple docstring"""
   from .. import __version__

   lowerCamelCase__       :						Optional[Any]        =							take_from
   lowerCamelCase__       :						Any        =							()
   if not isinstance(args[0]      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[int]        =							(args,)

   for attribute, version_name, message in args:
      if version.parse(version.parse(UpperCAmelCase					).base_version					) >= version.parse(UpperCAmelCase					):
         raise ValueError(
             f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
             f" version {__version__} is >= {version_name}"					)

      lowerCamelCase__       :						List[Any]        =							None
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and attribute in deprecated_kwargs:
         values += (deprecated_kwargs.pop(UpperCAmelCase					),)
         lowerCamelCase__       :						Tuple        =							f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
      elif hasattr(UpperCAmelCase      ,  UpperCAmelCase					):
         values += (getattr(UpperCAmelCase      ,  UpperCAmelCase					),)
         lowerCamelCase__       :						Union[str, Any]        =							f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
      elif deprecated_kwargs is None:
         lowerCamelCase__       :						int        =							f"`{attribute}` is deprecated and will be removed in version {version_name}."

      if warning is not None:
         lowerCamelCase__       :						int        =							warning + ''' ''' if standard_warn else ''''''
         warnings.warn(warning + message      ,  UpperCAmelCase      ,  stacklevel=UpperCAmelCase					)

   if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						Tuple        =							inspect.getouterframes(inspect.currentframe()					)[1]
      lowerCamelCase__       :						Optional[Any]        =							call_frame.filename
      lowerCamelCase__       :						List[Any]        =							call_frame.lineno
      lowerCamelCase__       :						Optional[Any]        =							call_frame.function
      lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							next(iter(deprecated_kwargs.items()					)					)
      raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"					)

   if len(UpperCAmelCase					) == 0:
      return
   elif len(UpperCAmelCase					) == 1:
      return values[0]
   return values



