



'''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      _lowerCAmelCase							:


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


 def    _a     (self							):
    A_     :						Dict     =					ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

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

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

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

    return config, input_ids, input_mask, token_labels


 def    _a     (self							):
    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=_UpperCAmelCase	,				initializer_range=self.initializer_range	,				pad_token_id=self.pad_token_id	,				)


 def    _a     (self							):
    A_     :						Dict     =					self.prepare_config_and_inputs()

    A_     :						Any     =					True

    return config, input_ids, input_mask, token_labels


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						List[Any]     =					GPTNeoXModel(config=_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						Dict     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase							)
    A_     :						Dict     =					model(_UpperCAmelCase							)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						Any     =					True
    A_     :						List[str]     =					GPTNeoXModel(_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						Tuple     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase							)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						Optional[Any]     =					GPTNeoXForCausalLM(config=_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						Optional[Any]     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				labels=_UpperCAmelCase							)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						Optional[int]     =					self.num_labels
    A_     :						Optional[int]     =					GPTNeoXForQuestionAnswering(_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						List[Any]     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase							)
    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    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						Tuple     =					self.num_labels
    A_     :						Any     =					GPTNeoXForSequenceClassification(_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						Optional[Any]     =					ids_tensor([self.batch_size]	,				self.type_sequence_label_size							)
    A_     :						List[str]     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				labels=_UpperCAmelCase							)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						Any     =					self.num_labels
    A_     :						Any     =					GPTNeoXForTokenClassification(_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()
    A_     :						List[Any]     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				labels=_UpperCAmelCase							)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
    A_     :						List[Any]     =					True
    A_     :						Union[str, Any]     =					GPTNeoXForCausalLM(config=_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)
    model.eval()

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

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

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

    A_     :						Any     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				output_hidden_states=_UpperCAmelCase							)
    A_     :						List[str]     =					output_from_no_past['hidden_states'][0]
    A_     :						str     =					model(
        _UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				past_key_values=_UpperCAmelCase	,				output_hidden_states=_UpperCAmelCase	,				)['hidden_states'][0]

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


 def    _a     (self							):
    A_     :						Union[str, Any]     =					self.prepare_config_and_inputs()
    A_     :						Dict     =					config_and_inputs
    A_     :						List[Any]     =					{'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict







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


 def    _a     (self							):
    A_     :						int     =					GPTNeoXModelTester(self							)
    A_     :						Tuple     =					ConfigTester(self	,				config_class=_UpperCAmelCase	,				hidden_size=64	,				num_attention_heads=8							)


 def    _a     (self							):
    self.config_tester.run_common_tests()


 def    _a     (self							):
    A_     :						Optional[int]     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(_UpperCAmelCase	,				_UpperCAmelCase	,				_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase	,				_UpperCAmelCase	,				_UpperCAmelCase							)


 def    _a     (self							):
    # This regression test was failing with PyTorch < 1.3
    A_     :						str     =					self.model_tester.prepare_config_and_inputs_for_decoder()

    A_     :						Union[str, Any]     =					None

    self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase	,				_UpperCAmelCase	,				_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						Optional[int]     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase	,				_UpperCAmelCase	,				_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						str     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						str     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase							)


 def    _a     (self							):
    A_     :						List[str]     =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase							)


 @unittest.skip(reason="""Feed forward chunking is not implemented"""							)
 def    _a     (self							):
    pass


 @parameterized.expand([("""linear""",), ("""dynamic""",)]							)
 def    _a     (self	,				lowercase							):
    A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs_for_common()
    A_     :						Optional[Any]     =					ids_tensor([1, 10]	,				config.vocab_size							)
    A_     :						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
    A_     :						Dict     =					GPTNeoXModel(_UpperCAmelCase							)
    original_model.to(_UpperCAmelCase							)
    original_model.eval()
    A_     :						Optional[int]     =					original_model(_UpperCAmelCase							).last_hidden_state
    A_     :						str     =					original_model(_UpperCAmelCase							).last_hidden_state

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

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







@require_torch
class      _lowerCAmelCase							(      unittest.TestCase			):


 @slow
 def    _a     (self							):
    A_     :						Tuple     =					AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)
    for checkpointing in [True, False]:
       A_     :						List[str]     =					GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)

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

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

       A_     :						int     =					model.generate(**_UpperCAmelCase	,				do_sample=_UpperCAmelCase	,				max_new_tokens=20							)
       A_     :						int     =					tokenizer.batch_decode(_UpperCAmelCase							)[0]

       self.assertEqual(_UpperCAmelCase	,				_UpperCAmelCase							)




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()