


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				:		Optional[Any]							,							__magic_name__				:		Any							,							__magic_name__				:		List[Any]=1_3							,							__magic_name__				:		str=7							,							__magic_name__				:		Tuple=True							,							__magic_name__				:		Dict=True							,							__magic_name__				:		Optional[int]=True							,							__magic_name__				:		str=True							,							__magic_name__				:		List[str]=9_9							,							__magic_name__				:		List[Any]=6_4							,							__magic_name__				:		Optional[Any]=5							,							__magic_name__				:		Optional[int]=4							,							__magic_name__				:		Dict=3_7							,							__magic_name__				:		Optional[int]="gelu"							,							__magic_name__				:		Optional[Any]=0.1							,							__magic_name__				:		Optional[Any]=0.1							,							__magic_name__				:		Dict=5_1_2							,							__magic_name__				:		List[Any]=1_6							,							__magic_name__				:		Optional[Any]=2							,							__magic_name__				:		Dict=0.02							,							__magic_name__				:		List[str]=3							,							__magic_name__				:		Any=4							,							__magic_name__				:		Dict=None							,							):







						"""simple docstring"""







						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 	lowerCamelCase_       (    self				:		List[str]    ):







						"""simple docstring"""







						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 	lowerCamelCase_       (    self				:		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=__lowercase							,							initializer_range=self.initializer_range							,							pad_token_id=self.pad_token_id							,							)




			def 	lowerCamelCase_       (    self				:		Any    ):







						"""simple docstring"""







						UpperCamelCase								=       self.prepare_config_and_inputs()

						UpperCamelCase								=       True

						return config, input_ids, input_mask, token_labels




			def 	lowerCamelCase_       (    self				:		Optional[int]							,							__magic_name__				:		int							,							__magic_name__				:		Union[str, Any]							,							__magic_name__				:		Optional[int]    ):







						"""simple docstring"""







						UpperCamelCase								=       GPTNeoXModel(config=__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase    )
						UpperCamelCase								=       model(__lowercase    )
						self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)    )




			def 	lowerCamelCase_       (    self				:		int							,							__magic_name__				:		Any							,							__magic_name__				:		List[Any]							,							__magic_name__				:		int    ):







						"""simple docstring"""







						UpperCamelCase								=       True
						UpperCamelCase								=       GPTNeoXModel(__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase    )
						self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)    )




			def 	lowerCamelCase_       (    self				:		List[Any]							,							__magic_name__				:		str							,							__magic_name__				:		List[str]							,							__magic_name__				:		Optional[Any]							,							__magic_name__				:		Any    ):







						"""simple docstring"""







						UpperCamelCase								=       GPTNeoXForCausalLM(config=__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase							,							labels=__lowercase    )
						self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)    )




			def 	lowerCamelCase_       (    self				:		int							,							__magic_name__				:		Union[str, Any]							,							__magic_name__				:		str							,							__magic_name__				:		Dict							,							__magic_name__				:		str    ):







						"""simple docstring"""







						UpperCamelCase								=       self.num_labels
						UpperCamelCase								=       GPTNeoXForQuestionAnswering(__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase    )
						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 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		List[Any]							,							__magic_name__				:		List[Any]							,							__magic_name__				:		List[str]							,							__magic_name__				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.num_labels
						UpperCamelCase								=       GPTNeoXForSequenceClassification(__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       ids_tensor([self.batch_size]							,							self.type_sequence_label_size    )
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase							,							labels=__lowercase    )
						self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)    )




			def 	lowerCamelCase_       (    self				:		Optional[int]							,							__magic_name__				:		int							,							__magic_name__				:		Union[str, Any]							,							__magic_name__				:		int							,							__magic_name__				:		Optional[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.num_labels
						UpperCamelCase								=       GPTNeoXForTokenClassification(__lowercase    )
						model.to(__lowercase    )
						model.eval()
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase							,							labels=__lowercase    )
						self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)    )




			def 	lowerCamelCase_       (    self				:		Optional[int]							,							__magic_name__				:		Dict							,							__magic_name__				:		Union[str, Any]							,							__magic_name__				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       True
						UpperCamelCase								=       GPTNeoXForCausalLM(config=__lowercase    )
						model.to(__lowercase    )
						model.eval()

						# first forward pass
						UpperCamelCase								=       model(__lowercase							,							attention_mask=__lowercase							,							use_cache=__lowercase    )
						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(__lowercase							,							attention_mask=__lowercase							,							output_hidden_states=__lowercase    )
						UpperCamelCase								=       output_from_no_past['hidden_states'][0]
						UpperCamelCase								=       model(
						    __lowercase							,							attention_mask=__lowercase							,							past_key_values=__lowercase							,							output_hidden_states=__lowercase							,							)['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(__lowercase							,							__lowercase							,							atol=1e-3    )    )





			def 	lowerCamelCase_       (    self				:		Union[str, Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.prepare_config_and_inputs()
						UpperCamelCase								=       config_and_inputs
						UpperCamelCase								=       {'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict




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




			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       GPTNeoXModelTester(self    )
						UpperCamelCase								=       ConfigTester(self							,							config_class=__lowercase							,							hidden_size=6_4							,							num_attention_heads=8    )




			def 	lowerCamelCase_       (    self				:		Optional[Any]    ):







						"""simple docstring"""







						self.config_tester.run_common_tests()




			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(__lowercase							,							__lowercase							,							__lowercase    )




			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(__lowercase							,							__lowercase							,							__lowercase    )




			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs_for_decoder()

						UpperCamelCase								=       None

						self.model_tester.create_and_check_model_as_decoder(__lowercase							,							__lowercase							,							__lowercase    )




			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowercase							,							__lowercase							,							__lowercase    )




			def 	lowerCamelCase_       (    self				:		str    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_causal_lm(*__lowercase    )




			def 	lowerCamelCase_       (    self				:		List[str]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__lowercase    )




			def 	lowerCamelCase_       (    self				:		Optional[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__lowercase    )




			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__lowercase    )




			@unittest.skip(reason="""Feed forward chunking is not implemented"""    )
			def 	lowerCamelCase_       (    self				:		Optional[Any]    ):







						"""simple docstring"""







						pass





			@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
			def 	lowerCamelCase_       (    self				:		List[Any]							,							__magic_name__				:		List[str]    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase								=       ids_tensor([1, 1_0]							,							config.vocab_size    )
						UpperCamelCase								=       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
						UpperCamelCase								=       GPTNeoXModel(__lowercase    )
						original_model.to(__lowercase    )
						original_model.eval()
						UpperCamelCase								=       original_model(__lowercase    ).last_hidden_state
						UpperCamelCase								=       original_model(__lowercase    ).last_hidden_state

						set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
						UpperCamelCase								=       {'type': scaling_type, 'factor': 1_0.0}
						UpperCamelCase								=       GPTNeoXModel(__lowercase    )
						scaled_model.to(__lowercase    )
						scaled_model.eval()
						UpperCamelCase								=       scaled_model(__lowercase    ).last_hidden_state
						UpperCamelCase								=       scaled_model(__lowercase    ).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(__lowercase							,							__lowercase							,							atol=1e-5    )    )
						else:
									self.assertFalse(torch.allclose(__lowercase							,							__lowercase							,							atol=1e-5    )    )

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




@require_torch
class  UpperCAmelCase       (							unittest.TestCase     ):




			@slow
			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						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(__lowercase    )

									UpperCamelCase								=       tokenizer("""My favorite food is"""							,							return_tensors="""pt"""    ).to(__lowercase    )
									# 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(**__lowercase							,							do_sample=__lowercase							,							max_new_tokens=2_0    )
									UpperCamelCase								=       tokenizer.batch_decode(__lowercase    )[0]

									self.assertEqual(__lowercase							,							__lowercase    )








'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class     _lowerCamelCase       :




 '''simple docstring'''


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.get_dummy_input()


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self.block_type == "down":
   return (4, 32, 16, 16)
  elif self.block_type == "mid":
   return (4, 32, 32, 32)
  elif self.block_type == "up":
   return (4, 32, 64, 64)

  raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""    )


 def 			snake_case__ (   self   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=False   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      4
  __A	:		List[Any]	  =      32
  __A	:		Optional[Any]	  =      (32, 32)

  __A	:		List[str]	  =      torch.manual_seed(0    )
  __A	:		Union[str, Any]	  =      torch.device(__lowercase    )
  __A	:		Optional[int]	  =      (batch_size, num_channels) + sizes
  __A	:		str	  =      randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    )
  __A	:		int	  =      {'hidden_states': hidden_states}

  if include_temb:
   __A	:		Optional[Any]	  =      128
   __A	:		Any	  =      randn_tensor((batch_size, temb_channels)   ,   generator=__lowercase   ,   device=__lowercase    )

  if include_res_hidden_states_tuple:
   __A	:		Any	  =      torch.manual_seed(1    )
   __A	:		List[Any]	  =      (randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    ),)

  if include_encoder_hidden_states:
   __A	:		str	  =      floats_tensor((batch_size, 32, 32)    ).to(__lowercase    )

  if include_skip_sample:
   __A	:		List[Any]	  =      randn_tensor(((batch_size, 3) + sizes)   ,   generator=__lowercase   ,   device=__lowercase    )

  return dummy_input


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {
      'in_channels': 32,
      'out_channels': 32,
      'temb_channels': 128,
  }
  if self.block_type == "up":
   __A	:		int	  =      32

  if self.block_type == "mid":
   init_dict.pop('out_channels'    )

  __A	:		Optional[int]	  =      self.dummy_input
  return init_dict, inputs_dict


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A  ,__A	:		List[Any]	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		int	  =      self.block_class(**__lowercase    )
  unet_block.to(__lowercase    )
  unet_block.eval()

  with torch.no_grad():
   __A	:		List[Any]	  =      unet_block(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Tuple	  =      output[0]

  self.assertEqual(output.shape   ,   self.output_shape    )

  __A	:		Any	  =      output[0, -1, -3:, -3:]
  __A	:		Dict	  =      torch.tensor(__lowercase    ).to(__lowercase    )
  assert torch_all_close(output_slice.flatten()   ,   __lowercase   ,   atol=5E-3    )







 @unittest.skipIf(torch_device == 'mps'   ,   'Training is not supported in mps'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		str	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		Optional[Any]	  =      self.block_class(**__lowercase    )
  model.to(__lowercase    )
  model.train()
  __A	:		Any	  =      model(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Any	  =      output[0]

  __A	:		int	  =      torch.device(__lowercase    )
  __A	:		Dict	  =      randn_tensor(output.shape   ,   device=__lowercase    )
  __A	:		Dict	  =      torch.nn.functional.mse_loss(__lowercase   ,   __lowercase    )
  loss.backward()






