






'''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 	UpperCamelCase__					:

		"""simple docstring"""




		def __init__( self						:						Optional[Any]							,    __A						:						Optional[int]							,    __A						:						Optional[int]=1_3							,    __A						:						List[Any]=7							,    __A						:						str=True							,    __A						:						Union[str, Any]=True							,    __A						:						int=True							,    __A						:						List[Any]=True							,    __A						:						Union[str, Any]=9_9							,    __A						:						Dict=6_4							,    __A						:						int=5							,    __A						:						List[str]=4							,    __A						:						Dict=3_7							,    __A						:						Any="gelu"							,    __A						:						str=0.1							,    __A						:						int=0.1							,    __A						:						int=5_1_2							,    __A						:						List[str]=1_6							,    __A						:						int=2							,    __A						:						Dict=0.0_2							,    __A						:						int=3							,    __A						:						Tuple=4							,    __A						:						Union[str, Any]=None							,    ):







							"""simple docstring"""
							_lowercase			= parent
							_lowercase			= batch_size
							_lowercase			= seq_length
							_lowercase			= is_training
							_lowercase			= use_input_mask
							_lowercase			= use_token_type_ids
							_lowercase			= use_labels
							_lowercase			= vocab_size
							_lowercase			= hidden_size
							_lowercase			= num_hidden_layers
							_lowercase			= num_attention_heads
							_lowercase			= intermediate_size
							_lowercase			= hidden_act
							_lowercase			= hidden_dropout_prob
							_lowercase			= attention_probs_dropout_prob
							_lowercase			= max_position_embeddings
							_lowercase			= type_vocab_size
							_lowercase			= type_sequence_label_size
							_lowercase			= initializer_range
							_lowercase			= num_labels
							_lowercase			= num_choices
							_lowercase			= scope
							_lowercase			= vocab_size - 1




		def       snake_case     ( self						:						Optional[int]    ):







							"""simple docstring"""
							_lowercase			= ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

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

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

							_lowercase			= self.get_config()

							return config, input_ids, input_mask, token_labels




		def       snake_case     ( 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=UpperCamelCase__							,    initializer_range=self.initializer_range							,    pad_token_id=self.pad_token_id							,    )




		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							_lowercase			= self.prepare_config_and_inputs()

							_lowercase			= True

							return config, input_ids, input_mask, token_labels




		def       snake_case     ( self						:						Union[str, Any]							,    __A						:						Any							,    __A						:						Optional[Any]							,    __A						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= GPTNeoXModel(config=UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__    )
							_lowercase			= model(UpperCamelCase__    )
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )




		def       snake_case     ( self						:						Dict							,    __A						:						str							,    __A						:						List[Any]							,    __A						:						Any    ):







							"""simple docstring"""
							_lowercase			= True
							_lowercase			= GPTNeoXModel(UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__    )
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )




		def       snake_case     ( self						:						Optional[int]							,    __A						:						int							,    __A						:						Any							,    __A						:						Tuple							,    __A						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= GPTNeoXForCausalLM(config=UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    labels=UpperCamelCase__    )
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )




		def       snake_case     ( self						:						List[str]							,    __A						:						Tuple							,    __A						:						str							,    __A						:						List[Any]							,    __A						:						Optional[int]    ):







							"""simple docstring"""
							_lowercase			= self.num_labels
							_lowercase			= GPTNeoXForQuestionAnswering(UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= 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       snake_case     ( self						:						Tuple							,    __A						:						List[str]							,    __A						:						Optional[int]							,    __A						:						Dict							,    __A						:						int    ):







							"""simple docstring"""
							_lowercase			= self.num_labels
							_lowercase			= GPTNeoXForSequenceClassification(UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    labels=UpperCamelCase__    )
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )




		def       snake_case     ( self						:						str							,    __A						:						Optional[int]							,    __A						:						Union[str, Any]							,    __A						:						Tuple							,    __A						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= self.num_labels
							_lowercase			= GPTNeoXForTokenClassification(UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    labels=UpperCamelCase__    )
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )




		def       snake_case     ( self						:						int							,    __A						:						Optional[Any]							,    __A						:						Union[str, Any]							,    __A						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= True
							_lowercase			= GPTNeoXForCausalLM(config=UpperCamelCase__    )
							model.to(UpperCamelCase__    )
							model.eval()

							# first forward pass
							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    use_cache=UpperCamelCase__    )
							_lowercase			= outputs.past_key_values

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

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

							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    output_hidden_states=UpperCamelCase__    )
							_lowercase			= output_from_no_past['''hidden_states'''][0]
							_lowercase			= model(
							    UpperCamelCase__							,    attention_mask=UpperCamelCase__							,    past_key_values=UpperCamelCase__							,    output_hidden_states=UpperCamelCase__							,    )['''hidden_states'''][0]

							# select random slice
							_lowercase			= ids_tensor((1,)							,    output_from_past.shape[-1]    ).item()
							_lowercase			= output_from_no_past[:, -3:, random_slice_idx].detach()
							_lowercase			= 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       snake_case     ( self						:						Optional[int]    ):







							"""simple docstring"""
							_lowercase			= self.prepare_config_and_inputs()
							_lowercase			= config_and_inputs
							_lowercase			= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict







@require_torch
class 	UpperCamelCase__					(     __SCREAMING_SNAKE_CASE      ,     __SCREAMING_SNAKE_CASE      ,     __SCREAMING_SNAKE_CASE      ,     unittest.TestCase			):

		"""simple docstring"""
		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						:						Dict    ):







							"""simple docstring"""
							_lowercase			= GPTNeoXModelTester(self    )
							_lowercase			= ConfigTester(self							,    config_class=UpperCamelCase__							,    hidden_size=6_4							,    num_attention_heads=8    )




		def       snake_case     ( self						:						str    ):







							"""simple docstring"""
							self.config_tester.run_common_tests()




		def       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		def       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs_for_decoder()

							_lowercase			= None

							self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		def       snake_case     ( self						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase__    )




		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__    )




		def       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__    )




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= 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       snake_case     ( self						:						int    ):







							"""simple docstring"""
							pass




		@parameterized.expand([("linear",), ("dynamic",)]    )
		def       snake_case     ( self						:						Optional[int]							,    __A						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs_for_common()
							_lowercase			= ids_tensor([1, 1_0]							,    config.vocab_size    )
							_lowercase			= 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
							_lowercase			= GPTNeoXModel(UpperCamelCase__    )
							original_model.to(UpperCamelCase__    )
							original_model.eval()
							_lowercase			= original_model(UpperCamelCase__    ).last_hidden_state
							_lowercase			= original_model(UpperCamelCase__    ).last_hidden_state

							set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
							_lowercase			= {'''type''': scaling_type, '''factor''': 1_0.0}
							_lowercase			= GPTNeoXModel(UpperCamelCase__    )
							scaled_model.to(UpperCamelCase__    )
							scaled_model.eval()
							_lowercase			= scaled_model(UpperCamelCase__    ).last_hidden_state
							_lowercase			= 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 	UpperCamelCase__					(     unittest.TestCase			):

		"""simple docstring"""




		@slow
		def       snake_case     ( self						:						Tuple    ):







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

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

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

												_lowercase			= model.generate(**UpperCamelCase__							,    do_sample=UpperCamelCase__							,    max_new_tokens=2_0    )
												_lowercase			= tokenizer.batch_decode(UpperCamelCase__    )[0]

												self.assertEqual(UpperCamelCase__							,    UpperCamelCase__    )



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


