





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_  :



					def __init__(		self     :     List[Any]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Tuple=13       ,      lowerCamelCase     :     Any=7       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     List[str]=True       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     Dict=99       ,      lowerCamelCase     :     List[Any]=64       ,      lowerCamelCase     :     Any=5       ,      lowerCamelCase     :     int=4       ,      lowerCamelCase     :     Optional[Any]=37       ,      lowerCamelCase     :     Dict="gelu"       ,      lowerCamelCase     :     Tuple=0.1       ,      lowerCamelCase     :     str=0.1       ,      lowerCamelCase     :     str=5_12       ,      lowerCamelCase     :     Dict=16       ,      lowerCamelCase     :     int=2       ,      lowerCamelCase     :     int=0.02       ,      lowerCamelCase     :     Optional[Any]=3       ,      lowerCamelCase     :     Optional[int]=4       ,      lowerCamelCase     :     Union[str, Any]=None       ,      ):
							lowerCamelCase_	:     str 						=      parent
							lowerCamelCase_	:     Dict 						=      batch_size
							lowerCamelCase_	:     Optional[int] 						=      seq_length
							lowerCamelCase_	:     Dict 						=      is_training
							lowerCamelCase_	:     List[str] 						=      use_input_mask
							lowerCamelCase_	:     Any 						=      use_token_type_ids
							lowerCamelCase_	:     Union[str, Any] 						=      use_labels
							lowerCamelCase_	:     Tuple 						=      vocab_size
							lowerCamelCase_	:     int 						=      hidden_size
							lowerCamelCase_	:     Dict 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[Any] 						=      num_attention_heads
							lowerCamelCase_	:     Optional[Any] 						=      intermediate_size
							lowerCamelCase_	:     int 						=      hidden_act
							lowerCamelCase_	:     Optional[Any] 						=      hidden_dropout_prob
							lowerCamelCase_	:     Tuple 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     Tuple 						=      max_position_embeddings
							lowerCamelCase_	:     int 						=      type_vocab_size
							lowerCamelCase_	:     List[Any] 						=      type_sequence_label_size
							lowerCamelCase_	:     Optional[int] 						=      initializer_range
							lowerCamelCase_	:     List[str] 						=      num_labels
							lowerCamelCase_	:     List[str] 						=      num_choices
							lowerCamelCase_	:     Dict 						=      scope
							lowerCamelCase_	:     Union[str, Any] 						=      vocab_size - 1



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     int 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size						)

							lowerCamelCase_	:     Optional[Any] 						=      None
							if self.use_input_mask:
									lowerCamelCase_	:     Union[str, Any] 						=      random_attention_mask([self.batch_size, self.seq_length]						)

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

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

							return config, input_ids, input_mask, token_labels



					def   __a     (		self     :     Union[str, Any]						):
							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=lowerCamelCase       ,      initializer_range=self.initializer_range       ,      pad_token_id=self.pad_token_id       ,      )



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     int 						=      self.prepare_config_and_inputs()

							lowerCamelCase_	:     List[Any] 						=      True

							return config, input_ids, input_mask, token_labels



					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      GPTNeoXModel(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[Any] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)						)



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     Tuple 						=      True
							lowerCamelCase_	:     str 						=      GPTNeoXModel(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     str						):
							lowerCamelCase_	:     List[Any] 						=      GPTNeoXForCausalLM(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)						)



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



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     int						):
							lowerCamelCase_	:     List[str] 						=      self.num_labels
							lowerCamelCase_	:     Tuple 						=      GPTNeoXForTokenClassification(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Any 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)						)



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     int						):
							lowerCamelCase_	:     Tuple 						=      True
							lowerCamelCase_	:     Optional[Any] 						=      GPTNeoXForCausalLM(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()

							# first forward pass
							lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      use_cache=lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      outputs.past_key_values

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

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

							lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      output_hidden_states=lowerCamelCase						)
							lowerCamelCase_	:     str 						=      output_from_no_past['hidden_states'][0]
							lowerCamelCase_	:     Optional[int] 						=      model(
							    lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      past_key_values=lowerCamelCase       ,      output_hidden_states=lowerCamelCase       ,      )['hidden_states'][0]

							# select random slice
							lowerCamelCase_	:     int 						=      ids_tensor((1,)       ,      output_from_past.shape[-1]						).item()
							lowerCamelCase_	:     Tuple 						=      output_from_no_past[:, -3:, random_slice_idx].detach()
							lowerCamelCase_	:     List[str] 						=      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(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-3						)						)





					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Dict 						=      self.prepare_config_and_inputs()
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      config_and_inputs
							lowerCamelCase_	:     List[str] 						=      {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						str						    =					(
					    (
					        GPTNeoXModel,
					        GPTNeoXForCausalLM,
					        GPTNeoXForQuestionAnswering,
					        GPTNeoXForSequenceClassification,
					        GPTNeoXForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					_a      :						Union[str, Any]						    =					(GPTNeoXForCausalLM,) if is_torch_available() else ()
					_a      :						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 {}
					)
					_a      :						Any						    =					False
					_a      :						Optional[Any]						    =					False
					_a      :						str						    =					False
					_a      :						Optional[int]						    =					False



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Union[str, Any] 						=      GPTNeoXModelTester(self						)
							lowerCamelCase_	:     List[Any] 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      hidden_size=64       ,      num_attention_heads=8						)



					def   __a     (		self     :     List[Any]						):
							self.config_tester.run_common_tests()



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)



					def   __a     (		self     :     List[Any]						):
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)



					def   __a     (		self     :     List[str]						):
							# This regression test was failing with PyTorch < 1.3
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      self.model_tester.prepare_config_and_inputs_for_decoder()

							lowerCamelCase_	:     Optional[Any] 						=      None

							self.model_tester.create_and_check_model_as_decoder(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Optional[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase						)



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     Tuple 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Tuple 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)



					def   __a     (		self     :     List[Any]						):
							lowerCamelCase_	:     str 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)



					@unittest.skip(reason='Feed forward chunking is not implemented'						)
					def   __a     (		self     :     Union[str, Any]						):
							pass





					@parameterized.expand([('linear',), ('dynamic',)]						)
					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Union[str, Any]						):
							lowerCamelCase_						,      lowerCamelCase_	:     Tuple 						=      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase_	:     str 						=      ids_tensor([1, 10]       ,      config.vocab_size						)
							lowerCamelCase_	:     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
							lowerCamelCase_	:     Tuple 						=      GPTNeoXModel(lowerCamelCase						)
							original_model.to(lowerCamelCase						)
							original_model.eval()
							lowerCamelCase_	:     int 						=      original_model(lowerCamelCase						).last_hidden_state
							lowerCamelCase_	:     List[Any] 						=      original_model(lowerCamelCase						).last_hidden_state

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

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






@require_torch
class 						UpperCamelCase_  (  unittest.TestCase							):



					@slow
					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Tuple 						=      AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'						)
							for checkpointing in [True, False]:
									lowerCamelCase_	:     Optional[int] 						=      GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'						)

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

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

									lowerCamelCase_	:     Tuple 						=      model.generate(**lowerCamelCase       ,      do_sample=lowerCamelCase       ,      max_new_tokens=20						)
									lowerCamelCase_	:     List[str] 						=      tokenizer.batch_decode(lowerCamelCase						)[0]

									self.assertEqual(lowerCamelCase       ,      lowerCamelCase						)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
