
"""simple docstring"""
import unittest

from parameterized import parameterized

from transformers import LlamaConfig, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class       lowerCAmelCase_		:



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_=13    ,				snake_case_=7    ,				snake_case_=True    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=True    ,				snake_case_=99    ,				snake_case_=32    ,				snake_case_=5    ,				snake_case_=4    ,				snake_case_=37    ,				snake_case_="gelu"    ,				snake_case_=0.1    ,				snake_case_=0.1    ,				snake_case_=512    ,				snake_case_=16    ,				snake_case_=2    ,				snake_case_=0.02    ,				snake_case_=3    ,				snake_case_=4    ,				snake_case_=None    ,				)       ->  List[str]:
													__lowerCAmelCase     =  parent
													__lowerCAmelCase     =  batch_size
													__lowerCAmelCase     =  seq_length
													__lowerCAmelCase     =  is_training
													__lowerCAmelCase     =  use_input_mask
													__lowerCAmelCase     =  use_token_type_ids
													__lowerCAmelCase     =  use_labels
													__lowerCAmelCase     =  vocab_size
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  intermediate_size
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  attention_probs_dropout_prob
													__lowerCAmelCase     =  max_position_embeddings
													__lowerCAmelCase     =  type_vocab_size
													__lowerCAmelCase     =  type_sequence_label_size
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  num_labels
													__lowerCAmelCase     =  num_choices
													__lowerCAmelCase     =  scope




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

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

													__lowerCAmelCase     =  None
													if self.use_token_type_ids:
																			__lowerCAmelCase     =  ids_tensor([self.batch_size, self.seq_length]    ,				self.type_vocab_size				)

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

													__lowerCAmelCase     =  self.get_config()

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def    A__						( self				)       ->  str:
													return LlamaConfig(
													    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=snake_case_    ,				initializer_range=self.initializer_range    ,				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  LlamaModel(config=snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_				)
													__lowerCAmelCase     =  model(snake_case_				)
													self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				)       ->  int:
													__lowerCAmelCase     =  True
													__lowerCAmelCase     =  LlamaModel(snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(
													    snake_case_    ,				attention_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				)
													__lowerCAmelCase     =  model(
													    snake_case_    ,				attention_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				)
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_				)
													self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				)       ->  Tuple:
													__lowerCAmelCase     =  LlamaForCausalLM(config=snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_    ,				labels=snake_case_				)
													self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				)       ->  Optional[Any]:
													__lowerCAmelCase     =  True
													__lowerCAmelCase     =  True
													__lowerCAmelCase     =  LlamaForCausalLM(config=snake_case_				)
													model.to(snake_case_				)
													model.eval()

													# first forward pass
													__lowerCAmelCase     =  model(
													    snake_case_    ,				attention_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				use_cache=snake_case_    ,				)
													__lowerCAmelCase     =  outputs.past_key_values

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

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

													__lowerCAmelCase     =  model(
													    snake_case_    ,				attention_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				output_hidden_states=snake_case_    ,				)["""hidden_states"""][0]
													__lowerCAmelCase     =  model(
													    snake_case_    ,				attention_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				past_key_values=snake_case_    ,				output_hidden_states=snake_case_    ,				)["""hidden_states"""][0]

													# select random slice
													__lowerCAmelCase     =  ids_tensor((1,)    ,				output_from_past.shape[-1]				).item()
													__lowerCAmelCase     =  output_from_no_past[:, -3:, random_slice_idx].detach()
													__lowerCAmelCase     =  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(snake_case_    ,				snake_case_    ,				atol=1e-3				)				)




							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  self.prepare_config_and_inputs()
													(
													    (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     (
													    __lowerCAmelCase
													)   ,     
													)     =  config_and_inputs
													__lowerCAmelCase     =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
													return config, inputs_dict
@require_torch
class       lowerCAmelCase_		(				A__				,				A__				,				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
							_snake_case						      =		(LlamaForCausalLM,) if is_torch_available() else ()
							_snake_case						      =		(
							    {
							        '''feature-extraction''': LlamaModel,
							        '''text-classification''': LlamaForSequenceClassification,
							        '''text-generation''': LlamaForCausalLM,
							        '''zero-shot''': LlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_snake_case						      =		False
							_snake_case						      =		False




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  LlamaModelTester(self				)
													__lowerCAmelCase     =  ConfigTester(self    ,				config_class=snake_case_    ,				hidden_size=37				)




							def    A__						( self				)       ->  Any:
													self.config_tester.run_common_tests()




							def    A__						( self				)       ->  Any:
													__lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*snake_case_				)




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			__lowerCAmelCase     =  type
																			self.model_tester.create_and_check_model(*snake_case_				)




							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase     =  3
													__lowerCAmelCase     =  input_dict["""input_ids"""]
													__lowerCAmelCase     =  input_ids.ne(1				).to(snake_case_				)
													__lowerCAmelCase     =  ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size				)
													__lowerCAmelCase     =  LlamaForSequenceClassification(snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_    ,				labels=snake_case_				)
													self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase     =  3
													__lowerCAmelCase     =  """single_label_classification"""
													__lowerCAmelCase     =  input_dict["""input_ids"""]
													__lowerCAmelCase     =  input_ids.ne(1				).to(snake_case_				)
													__lowerCAmelCase     =  ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size				)
													__lowerCAmelCase     =  LlamaForSequenceClassification(snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_    ,				labels=snake_case_				)
													self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase     =  3
													__lowerCAmelCase     =  """multi_label_classification"""
													__lowerCAmelCase     =  input_dict["""input_ids"""]
													__lowerCAmelCase     =  input_ids.ne(1				).to(snake_case_				)
													__lowerCAmelCase     =  ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]    ,				self.model_tester.type_sequence_label_size				).to(torch.float				)
													__lowerCAmelCase     =  LlamaForSequenceClassification(snake_case_				)
													model.to(snake_case_				)
													model.eval()
													__lowerCAmelCase     =  model(snake_case_    ,				attention_mask=snake_case_    ,				labels=snake_case_				)
													self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




							@unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""				)
							def    A__						( self				)       ->  List[Any]:
													pass




							@parameterized.expand([("""linear""",), ("""dynamic""",)]				)
							def    A__						( self    ,				snake_case_				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase     =  ids_tensor([1, 10]    ,				config.vocab_size				)
													__lowerCAmelCase     =  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     =  LlamaModel(snake_case_				)
													original_model.to(snake_case_				)
													original_model.eval()
													__lowerCAmelCase     =  original_model(snake_case_				).last_hidden_state
													__lowerCAmelCase     =  original_model(snake_case_				).last_hidden_state

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

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



							'''simple docstring'''





							@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
							@slow
							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
													__lowerCAmelCase     =  LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""    ,				device_map="""auto"""				)
													__lowerCAmelCase     =  model(torch.tensor([input_ids]				)				)
													# Expected mean on dim = -1
													__lowerCAmelCase     =  torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]				)
													torch.testing.assert_close(out.mean(-1				)    ,				snake_case_    ,				atol=1e-2    ,				rtol=1e-2				)
													# slicing logits[0, 0, 0:30]
													# fmt: off
													__lowerCAmelCase     =  torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]				)
													# fmt: on
													torch.testing.assert_close(out[0, 0, :30]    ,				snake_case_    ,				atol=1e-5    ,				rtol=1e-5				)




							@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
							@slow
							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
													__lowerCAmelCase     =  LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""    ,				device_map="""auto"""				)
													__lowerCAmelCase     =  model(torch.tensor(snake_case_				)				)
													# Expected mean on dim = -1
													__lowerCAmelCase     =  torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]				)
													torch.testing.assert_close(out.mean(-1				)    ,				snake_case_    ,				atol=1e-2    ,				rtol=1e-2				)
													# slicing logits[0, 0, 0:30]
													# fmt: off
													__lowerCAmelCase     =  torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]				)
													# fmt: on
													torch.testing.assert_close(out[0, 0, :30]    ,				snake_case_    ,				atol=1e-5    ,				rtol=1e-5				)




							@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
							@slow
							def    A__						( self				)       ->  Any:
													__lowerCAmelCase     =  [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
													__lowerCAmelCase     =  LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""    ,				device_map="""auto"""				)
													__lowerCAmelCase     =  model(torch.tensor(snake_case_				)				)
													# Expected mean on dim = -1
													__lowerCAmelCase     =  torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]				)
													torch.testing.assert_close(out.mean(-1				)    ,				snake_case_    ,				atol=1e-2    ,				rtol=1e-2				)
													# slicing logits[0, 0, 0:30]
													# fmt: off
													__lowerCAmelCase     =  torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]				)
													# fmt: on
													torch.testing.assert_close(out.mean(-1				)    ,				snake_case_    ,				atol=1e-2    ,				rtol=1e-2				)




							@unittest.skip(
							    """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""				)
							@slow
							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
													__lowerCAmelCase     =  LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""    ,				device_map="""auto"""				)
													__lowerCAmelCase     =  model(torch.tensor(snake_case_				)				)

													__lowerCAmelCase     =  torch.tensor(
													    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]    ,				dtype=torch.floataa				)
													torch.testing.assert_close(out.mean(-1				)    ,				snake_case_    ,				atol=1e-2    ,				rtol=1e-2				)
													# fmt: off
													__lowerCAmelCase     =  torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]				)
													# fmt: on
													torch.testing.assert_close(out[0, 0, :30]    ,				snake_case_    ,				atol=1e-5    ,				rtol=1e-5				)




							@unittest.skip("""Model is curently gated"""				)
							@slow
							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
													__lowerCAmelCase     =  """Simply put, the theory of relativity states that """
													__lowerCAmelCase     =  LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				)
													__lowerCAmelCase     =  tokenizer.encode(snake_case_    ,				return_tensors="""pt"""				)
													__lowerCAmelCase     =  LlamaForCausalLM.from_pretrained(
													    """meta-llama/Llama-2-13b-chat-hf"""    ,				device_map="""sequential"""    ,				use_safetensors=snake_case_				)

													# greedy generation outputs
													__lowerCAmelCase     =  model.generate(snake_case_    ,				max_new_tokens=64    ,				top_p=snake_case_    ,				temperature=1    ,				do_sample=snake_case_				)
													__lowerCAmelCase     =  tokenizer.decode(generated_ids[0]    ,				skip_special_tokens=snake_case_				)
													self.assertEqual(snake_case_    ,				snake_case_				)


"""simple docstring"""
import operator as op


SCREAMING_SNAKE_CASE_					     =	'''scaler.pt'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model'''
SCREAMING_SNAKE_CASE_					     =	'''random_states'''
SCREAMING_SNAKE_CASE_					     =	'''optimizer'''
SCREAMING_SNAKE_CASE_					     =	'''scheduler'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''1.10.2'''
SCREAMING_SNAKE_CASE_					     =	'''py38'''
SCREAMING_SNAKE_CASE_					     =	'''4.17.0'''
SCREAMING_SNAKE_CASE_					     =	['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
SCREAMING_SNAKE_CASE_					     =	['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
SCREAMING_SNAKE_CASE_					     =	['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
SCREAMING_SNAKE_CASE_					     =	'''2.0.1'''
SCREAMING_SNAKE_CASE_					     =	['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
SCREAMING_SNAKE_CASE_					     =	['''default''', '''reduce-overhead''', '''max-autotune''']

SCREAMING_SNAKE_CASE_					     =	{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
SCREAMING_SNAKE_CASE_					     =	[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
