






"""simple docstring"""



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
						import torch






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  Optional[int]     ,      snake_case :  List[str]     ,      snake_case :  Optional[int]=14     ,      snake_case :  List[str]=7     ,      snake_case :  Any=True     ,      snake_case :  Dict=True     ,      snake_case :  Optional[int]=False     ,      snake_case :  Optional[Any]=True     ,      snake_case :  Optional[int]=99     ,      snake_case :  Tuple=32     ,      snake_case :  int=4     ,      snake_case :  int=4     ,      snake_case :  Union[str, Any]=4     ,      snake_case :  int=37     ,      snake_case :  str="gelu"     ,      snake_case :  Union[str, Any]=0.1     ,      snake_case :  int=0.1     ,      snake_case :  Dict=512     ,      snake_case :  Optional[Any]=0.02     ,      ):
													'''simple docstring'''





													A__  :					str											=					parent
													A__  :					Dict											=					batch_size
													A__  :					Tuple											=					seq_length
													A__  :					List[Any]											=					is_training
													A__  :					int											=					use_input_mask
													A__  :					Optional[Any]											=					use_token_type_ids
													A__  :					Optional[Any]											=					use_labels
													A__  :					Dict											=					vocab_size
													A__  :					Tuple											=					hidden_size
													A__  :					int											=					rotary_dim
													A__  :					Dict											=					num_hidden_layers
													A__  :					List[str]											=					num_attention_heads
													A__  :					Tuple											=					intermediate_size
													A__  :					Union[str, Any]											=					hidden_act
													A__  :					Optional[int]											=					hidden_dropout_prob
													A__  :					int											=					attention_probs_dropout_prob
													A__  :					Tuple											=					max_position_embeddings
													A__  :					List[Any]											=					initializer_range
													A__  :					Optional[int]											=					None
													A__  :					List[str]											=					vocab_size - 1
													A__  :					Tuple											=					vocab_size - 1
													A__  :					List[str]											=					vocab_size - 1


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__  :					int											=					ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size							)

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

													A__  :					List[Any]											=					GPTJConfig(
													    vocab_size=self.vocab_size     ,      n_embd=self.hidden_size     ,      n_layer=self.num_hidden_layers     ,      n_head=self.num_attention_heads     ,      n_positions=self.max_position_embeddings     ,      use_cache=snake_case     ,      bos_token_id=self.bos_token_id     ,      eos_token_id=self.eos_token_id     ,      pad_token_id=self.pad_token_id     ,      rotary_dim=self.rotary_dim     ,      )

													return (config, input_ids, input_mask)


							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





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


							def   _UpperCamelCase    (     self :  List[Any]     ,      snake_case :  int     ,      snake_case :  Union[str, Any]     ,      snake_case :  Tuple     ,      snake_case :  Tuple							):
													'''simple docstring'''





													A__  :					List[str]											=					20
													A__  :					str											=					model_class_name(snake_case							)

													A__  :					List[str]											=					model.init_cache(input_ids.shape[0]     ,      snake_case							)
													A__  :					Dict											=					jnp.ones((input_ids.shape[0], max_decoder_length)     ,      dtype="""i4"""							)

													A__  :					Dict											=					jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1							)[None, :]     ,      (input_ids.shape[0], input_ids.shape[-1] - 1)							)
													A__  :					Any											=					model(
													    input_ids[:, :-1]     ,      attention_mask=snake_case     ,      past_key_values=snake_case     ,      position_ids=snake_case     ,      )

													A__  :					Dict											=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,      dtype="""i4"""							)
													A__  :					int											=					model(
													    input_ids[:, -1:]     ,      attention_mask=snake_case     ,      past_key_values=outputs_cache.past_key_values     ,      position_ids=snake_case     ,      )

													A__  :					Optional[int]											=					model(snake_case							)

													A__  :					Dict											=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
													self.parent.assertTrue(diff < 1e-3     ,      msg=F'Max diff is {diff}'							)


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  Union[str, Any]     ,      snake_case :  Any     ,      snake_case :  Any     ,      snake_case :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					int											=					20
													A__  :					Dict											=					model_class_name(snake_case							)

													A__  :					Tuple											=					jnp.concatenate(
													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]     ,      axis=-1     ,      )

													A__  :					List[str]											=					model.init_cache(input_ids.shape[0]     ,      snake_case							)
													A__  :					Dict											=					jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1							)[None, :]     ,      (input_ids.shape[0], input_ids.shape[-1] - 1)							)

													A__  :					Tuple											=					model(
													    input_ids[:, :-1]     ,      attention_mask=snake_case     ,      past_key_values=snake_case     ,      position_ids=snake_case     ,      )
													A__  :					Union[str, Any]											=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,      dtype="""i4"""							)
													A__  :					Any											=					model(
													    input_ids[:, -1:]     ,      past_key_values=outputs_cache.past_key_values     ,      attention_mask=snake_case     ,      position_ids=snake_case     ,      )

													A__  :					Dict											=					model(snake_case     ,      attention_mask=snake_case							)

													A__  :					List[str]											=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
													self.parent.assertTrue(diff < 1e-3     ,      msg=F'Max diff is {diff}'							)






@require_flax
class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase						):
							snake_case_       =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							snake_case_       =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					Optional[int]											=					FlaxGPTJModelTester(self							)


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													for model_class_name in self.all_model_classes:
																			A__					,					A__					,					A__  :					Tuple											=					self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(snake_case     ,      snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													for model_class_name in self.all_model_classes:
																			A__					,					A__					,					A__  :					List[Any]											=					self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    snake_case     ,      snake_case     ,      snake_case     ,      snake_case							)


							@tooslow
							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					List[str]											=					GPTaTokenizer.from_pretrained("""gpt2"""     ,      pad_token="""<|endoftext|>"""     ,      padding_side="""left"""							)
													A__  :					List[Any]											=					tokenizer(["""Hello this is a long string""", """Hey"""]     ,      return_tensors="""np"""     ,      padding=snake_case     ,      truncation=snake_case							)

													A__  :					Optional[Any]											=					FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""							)
													A__  :					List[Any]											=					False
													A__  :					Optional[int]											=					model.config.eos_token_id

													A__  :					str											=					jax.jit(model.generate							)

													A__  :					Any											=					jit_generate(
													    inputs["""input_ids"""]     ,      attention_mask=inputs["""attention_mask"""]     ,      pad_token_id=tokenizer.pad_token_id							).sequences

													A__  :					str											=					tokenizer.batch_decode(snake_case     ,      skip_special_tokens=snake_case							)

													A__  :					Optional[int]											=					[
													    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
													    """Hey, I'm a little late to the party. I'm going to""",
													]

													self.assertListEqual(snake_case     ,      snake_case							)


							@is_pt_flax_cross_test
							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													A__					,					A__  :					List[str]											=					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__							):
																									# prepare inputs
																									A__  :					str											=					self._prepare_for_class(snake_case     ,      snake_case							)
																									A__  :					Any											=					{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									A__  :					Tuple											=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									A__  :					str											=					getattr(snake_case     ,      snake_case							)

																									A__					,					A__  :					Union[str, Any]											=					pt_inputs["""input_ids"""].shape
																									A__  :					Dict											=					np.random.randint(0     ,      seq_length - 1     ,      size=(batch_size,)							)
																									for batch_idx, start_index in enumerate(snake_case							):
																															A__  :					List[Any]											=					0
																															A__  :					List[Any]											=					1
																															A__  :					Union[str, Any]											=					0
																															A__  :					Dict											=					1
																									A__  :					str											=					pt_model_class(snake_case							).eval()
																									A__  :					int											=					model_class(snake_case     ,      dtype=jnp.floataa							)

																									A__  :					str											=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,      snake_case							)
																									A__  :					int											=					fx_state

																									with torch.no_grad():
																															A__  :					Optional[Any]											=					pt_model(**snake_case							).to_tuple()

																									A__  :					Optional[int]											=					fx_model(**snake_case							).to_tuple()
																									self.assertEqual(len(snake_case							)     ,      len(snake_case							)     ,      """Output lengths differ between Flax and PyTorch"""							)
																									for fx_output, pt_output in zip(snake_case     ,      snake_case							):
																															self.assert_almost_equals(fx_output[:, -1]     ,      pt_output[:, -1].numpy()     ,      4e-2							)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															pt_model.save_pretrained(snake_case							)
																															A__  :					Any											=					model_class.from_pretrained(snake_case     ,      from_pt=snake_case							)

																									A__  :					List[str]											=					fx_model_loaded(**snake_case							).to_tuple()
																									self.assertEqual(
																									    len(snake_case							)     ,      len(snake_case							)     ,      """Output lengths differ between Flax and PyTorch"""							)
																									for fx_output_loaded, pt_output in zip(snake_case     ,      snake_case							):
																															self.assert_almost_equals(fx_output_loaded[:, -1]     ,      pt_output[:, -1].numpy()     ,      4e-2							)


							@is_pt_flax_cross_test
							def   _UpperCamelCase    (     self :  Optional[Any]							):
													'''simple docstring'''





													A__					,					A__  :					str											=					self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__							):
																									# prepare inputs
																									A__  :					List[str]											=					self._prepare_for_class(snake_case     ,      snake_case							)
																									A__  :					int											=					{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									A__  :					Dict											=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									A__  :					Union[str, Any]											=					getattr(snake_case     ,      snake_case							)

																									A__  :					Optional[Any]											=					pt_model_class(snake_case							).eval()
																									A__  :					Optional[int]											=					model_class(snake_case     ,      dtype=jnp.floataa							)

																									A__  :					Any											=					load_flax_weights_in_pytorch_model(snake_case     ,      fx_model.params							)
																									A__					,					A__  :					int											=					pt_inputs["""input_ids"""].shape
																									A__  :					Union[str, Any]											=					np.random.randint(0     ,      seq_length - 1     ,      size=(batch_size,)							)
																									for batch_idx, start_index in enumerate(snake_case							):
																															A__  :					Optional[Any]											=					0
																															A__  :					Tuple											=					1
																															A__  :					Any											=					0
																															A__  :					Tuple											=					1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																															A__  :					str											=					pt_model(**snake_case							).to_tuple()

																									A__  :					Union[str, Any]											=					fx_model(**snake_case							).to_tuple()
																									self.assertEqual(len(snake_case							)     ,      len(snake_case							)     ,      """Output lengths differ between Flax and PyTorch"""							)
																									for fx_output, pt_output in zip(snake_case     ,      snake_case							):
																															self.assert_almost_equals(fx_output[:, -1]     ,      pt_output[:, -1].numpy()     ,      4e-2							)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															fx_model.save_pretrained(snake_case							)
																															A__  :					str											=					pt_model_class.from_pretrained(snake_case     ,      from_flax=snake_case							)

																									with torch.no_grad():
																															A__  :					Optional[Any]											=					pt_model_loaded(**snake_case							).to_tuple()

																									self.assertEqual(
																									    len(snake_case							)     ,      len(snake_case							)     ,      """Output lengths differ between Flax and PyTorch"""							)
																									for fx_output, pt_output in zip(snake_case     ,      snake_case							):
																															self.assert_almost_equals(fx_output[:, -1]     ,      pt_output[:, -1].numpy()     ,      4e-2							)


							@tooslow
							def   _UpperCamelCase    (     self :  Optional[Any]							):
													'''simple docstring'''





													for model_class_name in self.all_model_classes:
																			A__  :					str											=					model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""							)
																			A__  :					str											=					model(np.ones((1, 1)							)							)
																			self.assertIsNotNone(snake_case							)














"""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 							__SCREAMING_SNAKE_CASE  :


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





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


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





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

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

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

													A__  :					Tuple											=					self.get_config()

													return config, input_ids, input_mask, token_labels


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''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=snake_case     ,      initializer_range=self.initializer_range     ,      pad_token_id=self.pad_token_id     ,      )


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





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

													A__  :					Union[str, Any]											=					True

													return config, input_ids, input_mask, token_labels


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  Optional[int]     ,      snake_case :  List[str]     ,      snake_case :  int							):
													'''simple docstring'''





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


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  str     ,      snake_case :  Any     ,      snake_case :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					int											=					True
													A__  :					str											=					GPTNeoXModel(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case							)
													self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)							)


							def   _UpperCamelCase    (     self :  Dict     ,      snake_case :  List[Any]     ,      snake_case :  str     ,      snake_case :  Optional[Any]     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Any											=					GPTNeoXForCausalLM(config=snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Optional[Any]     ,      snake_case :  Optional[Any]     ,      snake_case :  Union[str, Any]     ,      snake_case :  Tuple							):
													'''simple docstring'''





													A__  :					int											=					self.num_labels
													A__  :					int											=					GPTNeoXForQuestionAnswering(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Optional[Any]											=					model(snake_case     ,      attention_mask=snake_case							)
													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 :  str     ,      snake_case :  Tuple     ,      snake_case :  int     ,      snake_case :  int     ,      snake_case :  Dict							):
													'''simple docstring'''





													A__  :					List[Any]											=					self.num_labels
													A__  :					Tuple											=					GPTNeoXForSequenceClassification(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Any											=					ids_tensor([self.batch_size]     ,      self.type_sequence_label_size							)
													A__  :					List[str]											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)							)


							def   _UpperCamelCase    (     self :  Any     ,      snake_case :  Union[str, Any]     ,      snake_case :  int     ,      snake_case :  Tuple     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Tuple											=					self.num_labels
													A__  :					Any											=					GPTNeoXForTokenClassification(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Dict											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  List[str]     ,      snake_case :  Tuple     ,      snake_case :  Any							):
													'''simple docstring'''





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

													# first forward pass
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      use_cache=snake_case							)
													A__  :					str											=					outputs.past_key_values

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

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

													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      output_hidden_states=snake_case							)
													A__  :					List[Any]											=					output_from_no_past["""hidden_states"""][0]
													A__  :					List[str]											=					model(
													    snake_case     ,      attention_mask=snake_case     ,      past_key_values=snake_case     ,      output_hidden_states=snake_case     ,      )["""hidden_states"""][0]

													# select random slice
													A__  :					Tuple											=					ids_tensor((1,)     ,      output_from_past.shape[-1]							).item()
													A__  :					List[Any]											=					output_from_no_past[:, -3:, random_slice_idx].detach()
													A__  :					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(snake_case     ,      snake_case     ,      atol=1e-3							)							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





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






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


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					Any											=					GPTNeoXModelTester(self							)
													A__  :					Any											=					ConfigTester(self     ,      config_class=snake_case     ,      hidden_size=64     ,      num_attention_heads=8							)


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													self.config_tester.run_common_tests()


							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					Any											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					Dict											=					self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					List[str]											=					self.model_tester.prepare_config_and_inputs_for_decoder()

													A__  :					Optional[Any]											=					None

													self.model_tester.create_and_check_model_as_decoder(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					Union[str, Any]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Optional[Any]							):
													'''simple docstring'''





													A__  :					Optional[Any]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_causal_lm(*snake_case							)


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					Any											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__  :					Optional[Any]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__  :					List[str]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*snake_case							)


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





													pass


							@parameterized.expand([("""linear""",), ("""dynamic""",)]							)
							def   _UpperCamelCase    (     self :  Optional[Any]     ,      snake_case :  Optional[Any]							):
													'''simple docstring'''





													A__					,					A__  :					int											=					self.model_tester.prepare_config_and_inputs_for_common()
													A__  :					List[Any]											=					ids_tensor([1, 10]     ,      config.vocab_size							)
													A__  :					str											=					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__  :					Union[str, Any]											=					GPTNeoXModel(snake_case							)
													original_model.to(snake_case							)
													original_model.eval()
													A__  :					Optional[int]											=					original_model(snake_case							).last_hidden_state
													A__  :					List[str]											=					original_model(snake_case							).last_hidden_state

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

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






@require_torch
class 							__SCREAMING_SNAKE_CASE  (			unittest.TestCase						):


							@slow
							def   _UpperCamelCase    (     self :  Tuple							):
													'''simple docstring'''





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

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

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

																			A__  :					Tuple											=					model.generate(**snake_case     ,      do_sample=snake_case     ,      max_new_tokens=20							)
																			A__  :					Tuple											=					tokenizer.batch_decode(snake_case							)[0]

																			self.assertEqual(snake_case     ,      snake_case							)






