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  A_			:

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

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

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

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

   __a							=	self.get_config()

   return config, input_ids, input_mask, token_labels

  def 		_UpperCAmelCase (		self			:   List[str]							):
   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=__SCREAMING_SNAKE_CASE  ,				initializer_range=self.initializer_range  ,				pad_token_id=self.pad_token_id  ,				)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							,  __a							,  __a							,  __a							=	self.prepare_config_and_inputs()

   __a							=	True

   return config, input_ids, input_mask, token_labels

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

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   __a							=	True
   __a							=	GPTNeoXModel(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_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]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any							):
   __a							=	GPTNeoXForCausalLM(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)							)

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

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

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	True
   __a							=	GPTNeoXForCausalLM(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()

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

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

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

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

   # select random slice
   __a							=	ids_tensor((1,)  ,				output_from_past.shape[-1]							).item()
   __a							=	output_from_no_past[:, -3:, random_slice_idx].detach()
   __a							=	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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	self.prepare_config_and_inputs()
   __a							,  __a							,  __a							,  __a							=	config_and_inputs
   __a							=	{"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict
@require_torch
class  A_			(							a_      ,							a_      ,							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      (
      (
          GPTNeoXModel,
          GPTNeoXForCausalLM,
          GPTNeoXForQuestionAnswering,
          GPTNeoXForSequenceClassification,
          GPTNeoXForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE     					=      (GPTNeoXForCausalLM,) if is_torch_available() else ()
  _SCREAMING_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 {}
  )
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	GPTNeoXModelTester(self							)
   __a							=	ConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				hidden_size=64  ,				num_attention_heads=8							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							,  __a							,  __a							,  __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							,  __a							,  __a							,  __a							=	self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   # This regression test was failing with PyTorch < 1.3
   __a							,  __a							,  __a							,  __a							=	self.model_tester.prepare_config_and_inputs_for_decoder()

   __a							=	None

   self.model_tester.create_and_check_model_as_decoder(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							,  __a							,  __a							,  __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_causal_lm(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE							)

  @unittest.skip(reason="Feed forward chunking is not implemented"							)
  def 		_UpperCAmelCase (		self			:   Tuple							):
   pass

  @parameterized.expand([("linear",), ("dynamic",)]							)
  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							,  __a							=	self.model_tester.prepare_config_and_inputs_for_common()
   __a							=	ids_tensor([1, 10]  ,				config.vocab_size							)
   __a							=	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							=	GPTNeoXModel(__SCREAMING_SNAKE_CASE							)
   original_model.to(__SCREAMING_SNAKE_CASE							)
   original_model.eval()
   __a							=	original_model(__SCREAMING_SNAKE_CASE							).last_hidden_state
   __a							=	original_model(__SCREAMING_SNAKE_CASE							).last_hidden_state

   set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
   __a							=	{"type": scaling_type, "factor": 10.0}
   __a							=	GPTNeoXModel(__SCREAMING_SNAKE_CASE							)
   scaled_model.to(__SCREAMING_SNAKE_CASE							)
   scaled_model.eval()
   __a							=	scaled_model(__SCREAMING_SNAKE_CASE							).last_hidden_state
   __a							=	scaled_model(__SCREAMING_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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-5							)							)
   else:
    self.assertFalse(torch.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-5							)							)

   # The output should be different for long inputs
   self.assertFalse(torch.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-5							)							)
@require_torch
class  A_			(							unittest.TestCase			):

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

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

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

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

    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)



import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_logger(__name__)
class  A_			(							enum.Enum			):
  _SCREAMING_SNAKE_CASE     					=      """all_checks"""
  _SCREAMING_SNAKE_CASE     					=      """basic_checks"""
  _SCREAMING_SNAKE_CASE     					=      """no_checks"""
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A				,					_A=None       ):



 """simple docstring"""


 if expected_checksums is None:
  logger.info("Unable to verify checksums."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreDownloadedFiles(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedDownloadedFile(str(set(_A       ) - set(_A       )       )       )
 __a							=	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
 __a							=	" for " + verification_name if verification_name is not None else ""
 if len(_A       ) > 0:
  raise NonMatchingChecksumError(
      f"""Checksums didn't match{for_verification_name}:\n"""
      f"""{bad_urls}\n"""
      "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"       )
 logger.info("All the checksums matched successfully" + for_verification_name       )
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 if expected_splits is None:
  logger.info("Unable to verify splits sizes."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreSplits(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedSplits(str(set(_A       ) - set(_A       )       )       )
 __a							=	[
     {"expected": expected_splits[name], "recorded": recorded_splits[name]}
     for name in expected_splits
     if expected_splits[name].num_examples != recorded_splits[name].num_examples
 ]
 if len(_A       ) > 0:
  raise NonMatchingSplitsSizesError(str(_A       )       )
 logger.info("All the splits matched successfully."       )



def 	__A		(      _A				,					_A = True       ):



 """simple docstring"""


 if record_checksum:
  __a							=	shaaaa()
  with open(_A				,					"rb"       ) as f:
   for chunk in iter(lambda: f.read(1 << 20       )				,					B""       ):
    m.update(_A       )
   __a							=	m.hexdigest()
 else:
  __a							=	None
 return {"num_bytes": os.path.getsize(_A       ), "checksum": checksum}






def 	__A		(      _A       ):



 """simple docstring"""


 if dataset_size and config.IN_MEMORY_MAX_SIZE:
  return dataset_size < config.IN_MEMORY_MAX_SIZE
 else:
  return False


