


'''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      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=5      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=16      ,      __lowercase=2      ,      __lowercase=0.0_2      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=None      ,      ):

  """simple docstring"""







  __A  :		List[str]       =			parent
  __A  :		List[Any]       =			batch_size
  __A  :		Optional[Any]       =			seq_length
  __A  :		Optional[int]       =			is_training
  __A  :		Union[str, Any]       =			use_input_mask
  __A  :		Optional[Any]       =			use_token_type_ids
  __A  :		Tuple       =			use_labels
  __A  :		List[str]       =			vocab_size
  __A  :		str       =			hidden_size
  __A  :		Any       =			num_hidden_layers
  __A  :		List[Any]       =			num_attention_heads
  __A  :		int       =			intermediate_size
  __A  :		str       =			hidden_act
  __A  :		Tuple       =			hidden_dropout_prob
  __A  :		int       =			attention_probs_dropout_prob
  __A  :		str       =			max_position_embeddings
  __A  :		Union[str, Any]       =			type_vocab_size
  __A  :		Dict       =			type_sequence_label_size
  __A  :		List[str]       =			initializer_range
  __A  :		str       =			num_labels
  __A  :		int       =			num_choices
  __A  :		Optional[Any]       =			scope


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

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

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

  __A  :		Dict       =			None
  __A  :		List[Any]       =			None
  __A  :		int       =			None
  if self.use_labels:
   __A  :		Any       =			ids_tensor([self.batch_size]      ,      self.type_sequence_label_size						)
   __A  :		Optional[int]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels						)
   __A  :		Optional[Any]       =			ids_tensor([self.batch_size]      ,      self.num_choices						)

  __A  :		Any       =			self.get_config()

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


 def  snake_case__   (      self						):

  """simple docstring"""







  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=__lowercase      ,      initializer_range=self.initializer_range      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			LlamaModel(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		List[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
  __A  :		List[str]       =			model(__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		Any       =			True
  __A  :		List[Any]       =			LlamaModel(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		List[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      )
  __A  :		Tuple       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      )
  __A  :		List[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		Dict       =			LlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[int]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[Any]       =			True
  __A  :		Any       =			True
  __A  :		Union[str, Any]       =			LlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()

  # first forward pass
  __A  :		Dict       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      use_cache=__lowercase      ,      )
  __A  :		Any       =			outputs.past_key_values

  # create hypothetical multiple next token and extent to next_input_ids
  __A  :		Optional[int]       =			ids_tensor((self.batch_size, 3)      ,      config.vocab_size						)
  __A  :		int       =			ids_tensor((self.batch_size, 3)      ,      vocab_size=2						)

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

  __A  :		Optional[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]
  __A  :		Dict       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      past_key_values=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]

  # select random slice
  __A  :		List[Any]       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
  __A  :		List[str]       =			output_from_no_past[:, -3:, random_slice_idx].detach()
  __A  :		int       =			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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			self.prepare_config_and_inputs()
  (
      (
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,
  )  :		Dict       =			config_and_inputs
  __A  :		int       =			{'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		str    							=						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __lowercase				:		Optional[int]    							=						(LlamaForCausalLM,) if is_torch_available() else ()
 __lowercase				:		Any    							=						(
     {
         '''feature-extraction''': LlamaModel,
         '''text-classification''': LlamaForSequenceClassification,
         '''text-generation''': LlamaForCausalLM,
         '''zero-shot''': LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase				:		List[Any]    							=						False
 __lowercase				:		List[str]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			LlamaModelTester(self						)
  __A  :		Any       =			ConfigTester(self      ,      config_class=__lowercase      ,      hidden_size=37						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   __A  :		int       =			type
   self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		List[Any]       =			3
  __A  :		Union[str, Any]       =			input_dict['input_ids']
  __A  :		Union[str, Any]       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Tuple       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Any       =			LlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[int]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Any       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Any       =			3
  __A  :		Dict       =			'single_label_classification'
  __A  :		Any       =			input_dict['input_ids']
  __A  :		Dict       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Optional[Any]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		int       =			LlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Tuple       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		str       =			3
  __A  :		Optional[int]       =			'multi_label_classification'
  __A  :		int       =			input_dict['input_ids']
  __A  :		Optional[Any]       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		int       =			ids_tensor(
      [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size						).to(torch.float						)
  __A  :		List[Any]       =			LlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  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  snake_case__   (      self						):

  """simple docstring"""







  pass







 @parameterized.expand([('linear',), ('dynamic',)]						)
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A     ,__A  :		Union[str, Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		List[str]       =			ids_tensor([1, 10]      ,      config.vocab_size						)
  __A  :		Dict       =			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  :		Optional[int]       =			LlamaModel(__lowercase						)
  original_model.to(__lowercase						)
  original_model.eval()
  __A  :		int       =			original_model(__lowercase						).last_hidden_state
  __A  :		str       =			original_model(__lowercase						).last_hidden_state

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		List[Any]       =			{'type': scaling_type, 'factor': 1_0.0}
  __A  :		str       =			LlamaModel(__lowercase						)
  scaled_model.to(__lowercase						)
  scaled_model.eval()
  __A  :		Union[str, Any]       =			scaled_model(__lowercase						).last_hidden_state
  __A  :		Optional[Any]       =			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 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
  __A  :		List[str]       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'      ,      device_map='auto'						)
  __A  :		Optional[Any]       =			model(torch.tensor([input_ids]						)						)
  # Expected mean on dim = -1
  __A  :		int       =			torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]						)
  torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
  # slicing logits[0, 0, 0:30]
  # fmt: off
  __A  :		List[str]       =			torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]						)
  # fmt: on
  torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      atol=1E-5      ,      rtol=1E-5						)


 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
  __A  :		int       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'      ,      device_map='auto'						)
  __A  :		Union[str, Any]       =			model(torch.tensor(__lowercase						)						)
  # Expected mean on dim = -1
  __A  :		Tuple       =			torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]						)
  torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
  # slicing logits[0, 0, 0:30]
  # fmt: off
  __A  :		int       =			torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]						)
  # fmt: on
  torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      atol=1E-5      ,      rtol=1E-5						)


 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
  __A  :		int       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      ,      device_map='auto'						)
  __A  :		str       =			model(torch.tensor(__lowercase						)						)
  # Expected mean on dim = -1
  __A  :		Union[str, Any]       =			torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]						)
  torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
  # slicing logits[0, 0, 0:30]
  # fmt: off
  __A  :		str       =			torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]						)
  # fmt: on
  torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
  __A  :		List[str]       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'      ,      device_map='auto'						)
  __A  :		Union[str, Any]       =			model(torch.tensor(__lowercase						)						)

  __A  :		List[str]       =			torch.tensor(
      [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]      ,      dtype=torch.floataa						)
  torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
  # fmt: off
  __A  :		List[Any]       =			torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]						)
  # fmt: on
  torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      atol=1E-5      ,      rtol=1E-5						)







 @unittest.skip('Model is curently gated'						)
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			'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'
  __A  :		Any       =			'Simply put, the theory of relativity states that '
  __A  :		Tuple       =			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						)
  __A  :		List[Any]       =			tokenizer.encode(__lowercase      ,      return_tensors='pt'						)
  __A  :		List[Any]       =			LlamaForCausalLM.from_pretrained(
      'meta-llama/Llama-2-13b-chat-hf'      ,      device_map='sequential'      ,      use_safetensors=__lowercase						)

  # greedy generation outputs
  __A  :		Optional[int]       =			model.generate(__lowercase      ,      max_new_tokens=64      ,      top_p=__lowercase      ,      temperature=1      ,      do_sample=__lowercase						)
  __A  :		str       =			tokenizer.decode(generated_ids[0]      ,      skip_special_tokens=__lowercase						)
  self.assertEqual(__lowercase      ,      __lowercase						)










'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

 __A  :		Union[str, Any]       =			Path(UpperCamelCase__			)
 __A  :		Optional[int]       =			save_dir.joinpath(f"""rank_{local_rank}_output.json"""			)
 torch.cuda.set_device(UpperCamelCase__			)
 __A  :		Dict       =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__			).cuda()
 if fpaa:
  __A  :		Optional[Any]       =			model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(UpperCamelCase__,				UpperCamelCase__			)  # update config with task specific params
 __A  :		Any       =			generate_kwargs.pop('num_beams',				model.config.num_beams			)  # AttributeError risk?
 if num_return_sequences > num_beams:
  __A  :		int       =			num_return_sequences

 __A  :		Optional[Any]       =			AutoTokenizer.from_pretrained(UpperCamelCase__			)
 logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __A  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			argparse.ArgumentParser(
     epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'			)
 parser.add_argument('--data_dir',				type=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 parser.add_argument('--task',				type=UpperCamelCase__,				default='summarization',				help='used for task_specific_params + metrics'			)
 parser.add_argument('--bs',				type=UpperCamelCase__,				default=8,				required=UpperCamelCase__,				help='batch size'			)
 parser.add_argument(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				help='will be added to the begininng of src examples'			)
 parser.add_argument('--fp16',				action='store_true'			)
 parser.add_argument('--debug',				action='store_true'			)
 __A  :		int       =			time.time()
 __A     ,__A  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			list(json_save_dir.glob('rank_*.json'			)			)
 if intermediate_files:
  raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""			)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __A  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				args.model_name,				type_path=args.type_path,				bs=args.bs,				fpaa=args.fpaa,				task=args.task,				local_rank=args.local_rank,				n_obs=args.n_obs,				max_source_length=args.max_source_length,				num_return_sequences=args.num_return_sequences,				prefix=args.prefix,				dataset_kwargs=UpperCamelCase__,				**UpperCamelCase__,				)

 if args.local_rank <= 0:
  __A  :		Tuple       =			Path(args.save_dir			)
  save_dir.mkdir(exist_ok=UpperCamelCase__			)
  __A  :		Dict       =			gather_results_from_each_node(UpperCamelCase__,				UpperCamelCase__,				args.sync_timeout			)
  __A  :		Union[str, Any]       =			combine_partial_results(UpperCamelCase__			)
  if args.num_return_sequences > 1:
   __A  :		Any       =			save_dir.joinpath('pseudolabel_results.json'			)
   print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""			)
   save_json(UpperCamelCase__,				UpperCamelCase__			)
   return
  __A  :		int       =			Path(args.data_dir			).joinpath(args.type_path + '.target'			)
  with open(UpperCamelCase__			) as f:
   __A  :		str       =			[x.rstrip() for x in f.readlines()][: len(UpperCamelCase__			)]

  # Calculate metrics, save metrics,  and save _generations.txt
  __A  :		Tuple       =			'translation' in args.task
  __A  :		Union[str, Any]       =			calculate_bleu if calc_bleu else calculate_rouge
  __A  :		Optional[Any]       =			'bleu' if calc_bleu else 'rouge'
  __A  :		Dict       =			score_fn(UpperCamelCase__,				UpperCamelCase__			)
  __A  :		Any       =			len(UpperCamelCase__			)
  __A  :		Tuple       =			time.time() - start_time
  __A  :		Tuple       =			round(runtime / metrics['n_obs'],				4			)
  __A  :		int       =			num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __A  :		List[Any]       =			save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""			)
  save_json(UpperCamelCase__,				UpperCamelCase__,				indent=UpperCamelCase__			)
  print(UpperCamelCase__			)
  write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}_generations.txt"""			)			)
  if args.debug:
   write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}.target"""			)			)
  else:
   shutil.rmtree(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('Rank 0 gave up on waiting for other processes'			)
 # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()






