





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


lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			{
    "MIT/ast-finetuned-audioset-10-10-0.4593": (
        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
    ),
}





class        _lowerCamelCase      (       __lowerCAmelCase ):
						UpperCAmelCase_ =			"audio-spectrogram-transformer"


						def __init__(self   ,							__a=7_68   ,							__a=12   ,							__a=12   ,							__a=30_72   ,							__a="gelu"   ,							__a=0.0   ,							__a=0.0   ,							__a=0.02   ,							__a=1e-1_2   ,							__a=16   ,							__a=True   ,							__a=10   ,							__a=10   ,							__a=10_24   ,							__a=1_28   ,							**__a   ,							) ->   Optional[int]:
								super().__init__(**lowerCamelCase__		)

								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	num_hidden_layers
								UpperCamelCase   =	num_attention_heads
								UpperCamelCase   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	layer_norm_eps
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	qkv_bias
								UpperCamelCase   =	frequency_stride
								UpperCamelCase   =	time_stride
								UpperCamelCase   =	max_length
								UpperCamelCase   =	num_mel_bins













"""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   ,							__a   ,							__a=13   ,							__a=7   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=99   ,							__a=64   ,							__a=5   ,							__a=4   ,							__a=37   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=5_12   ,							__a=16   ,							__a=2   ,							__a=0.02   ,							__a=3   ,							__a=4   ,							__a=None   ,							) ->   Optional[int]:
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	seq_length
								UpperCamelCase   =	is_training
								UpperCamelCase   =	use_input_mask
								UpperCamelCase   =	use_token_type_ids
								UpperCamelCase   =	use_labels
								UpperCamelCase   =	vocab_size
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	num_hidden_layers
								UpperCamelCase   =	num_attention_heads
								UpperCamelCase   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	max_position_embeddings
								UpperCamelCase   =	type_vocab_size
								UpperCamelCase   =	type_sequence_label_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	num_labels
								UpperCamelCase   =	num_choices
								UpperCamelCase   =	scope
								UpperCamelCase   =	vocab_size - 1


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

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

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

								UpperCamelCase   =	self.get_config()

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self		) ->   int:
								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     snake_case_   (self		) ->   int:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.prepare_config_and_inputs()

								UpperCamelCase   =	True

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	GPTNeoXModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXModel(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForQuestionAnswering(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForSequenceClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	ids_tensor([self.batch_size]   ,							self.type_sequence_label_size		)
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForTokenClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()

								# first forward pass
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							use_cache=__a		)
								UpperCamelCase   =	outputs.past_key_values

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

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

								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							output_hidden_states=__a		)
								UpperCamelCase   =	output_from_no_past["hidden_states"][0]
								UpperCamelCase   =	model(
								    __a   ,							attention_mask=__a   ,							past_key_values=__a   ,							output_hidden_states=__a   ,							)["hidden_states"][0]

								# select random slice
								UpperCamelCase   =	ids_tensor((1,)   ,							output_from_past.shape[-1]		).item()
								UpperCamelCase   =	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCamelCase   =	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     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict





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


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	GPTNeoXModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							hidden_size=64   ,							num_attention_heads=8		)


						def     snake_case_   (self		) ->   int:
								self.config_tester.run_common_tests()


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(__a   ,							__a   ,							__a		)


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

								UpperCamelCase   =	None

								self.model_tester.create_and_check_model_as_decoder(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	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     snake_case_   (self		) ->   Any:
								pass




						@parameterized.expand([("linear",), ("dynamic",)]		)
						def     snake_case_   (self   ,							__a		) ->   Dict:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCamelCase   =	ids_tensor([1, 10]   ,							config.vocab_size		)
								UpperCamelCase   =	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
								UpperCamelCase   =	GPTNeoXModel(__a		)
								original_model.to(__a		)
								original_model.eval()
								UpperCamelCase   =	original_model(__a		).last_hidden_state
								UpperCamelCase   =	original_model(__a		).last_hidden_state

								set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
								UpperCamelCase   =	{"type": scaling_type, "factor": 10.0}
								UpperCamelCase   =	GPTNeoXModel(__a		)
								scaled_model.to(__a		)
								scaled_model.eval()
								UpperCamelCase   =	scaled_model(__a		).last_hidden_state
								UpperCamelCase   =	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        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"		)
								for checkpointing in [True, False]:
										UpperCamelCase   =	GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"		)

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

										UpperCamelCase   =	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
										UpperCamelCase   =	"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

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

										self.assertEqual(__a   ,							__a		)






