




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 		lowercase							:

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

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Tuple    =		ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size       )

           _A							:  Optional[int]    =		None
           if self.use_input_mask:
                 _A							:  str    =		random_attention_mask([self.batch_size, self.seq_length]       )

           _A							:  List[Any]    =		None
           if self.use_token_type_ids:
                 _A							:  Dict    =		ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size       )

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

           _A							:  List[str]    =		self.get_config()

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

     def       a__			(						self       )		->							List[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=_a	,  initializer_range=self.initializer_range	,  )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							str:
           _A							:  Optional[int]    =		LlamaModel(config=_a       )
           model.to(_a       )
           model.eval()
           _A							:  Optional[Any]    =		model(_a	,  attention_mask=_a       )
           _A							:  Optional[Any]    =		model(_a       )
           self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							Any:
           _A							:  Optional[int]    =		True
           _A							:  Optional[Any]    =		LlamaModel(_a       )
           model.to(_a       )
           model.eval()
           _A							:  Dict    =		model(
               _a	,  attention_mask=_a	,  encoder_hidden_states=_a	,  encoder_attention_mask=_a	,  )
           _A							:  Dict    =		model(
               _a	,  attention_mask=_a	,  encoder_hidden_states=_a	,  )
           _A							:  Union[str, Any]    =		model(_a	,  attention_mask=_a       )
           self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							int:
           _A							:  Optional[int]    =		LlamaForCausalLM(config=_a       )
           model.to(_a       )
           model.eval()
           _A							:  int    =		model(_a	,  attention_mask=_a	,  labels=_a       )
           self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							Tuple:
           _A							:  List[str]    =		True
           _A							:  Any    =		True
           _A							:  str    =		LlamaForCausalLM(config=_a       )
           model.to(_a       )
           model.eval()

           # first forward pass
           _A							:  int    =		model(
               _a	,  attention_mask=_a	,  encoder_hidden_states=_a	,  encoder_attention_mask=_a	,  use_cache=_a	,  )
           _A							:  Optional[int]    =		outputs.past_key_values

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

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

           _A							:  Optional[Any]    =		model(
               _a	,  attention_mask=_a	,  encoder_hidden_states=_a	,  encoder_attention_mask=_a	,  output_hidden_states=_a	,  )["""hidden_states"""][0]
           _A							:  List[Any]    =		model(
               _a	,  attention_mask=_a	,  encoder_hidden_states=_a	,  encoder_attention_mask=_a	,  past_key_values=_a	,  output_hidden_states=_a	,  )["""hidden_states"""][0]

           # select random slice
           _A							:  Tuple    =		ids_tensor((1,)	,  output_from_past.shape[-1]       ).item()
           _A							:  List[Any]    =		output_from_no_past[:, -3:, random_slice_idx].detach()
           _A							:  List[Any]    =		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(_a	,  _a	,  atol=1e-3       )       )



     def       a__			(						self       )		->							Dict:
           _A							:  Any    =		self.prepare_config_and_inputs()
           (
               (
               _A
           )    ,     (
               _A
           )    ,     (
               _A
           )    ,     (
               _A
           )    ,     (
               _A
           )    ,     (
               _A
           )    ,     (
               _A
           )    ,     
           )							:  Optional[Any]    =		config_and_inputs
           _A							:  Any    =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict







@require_torch
class 		lowercase							(     UpperCamelCase__,UpperCamelCase__,UpperCamelCase__,unittest.TestCase	):
     _a											=		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     _a											=		(LlamaForCausalLM,) if is_torch_available() else ()
     _a											=		(
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _a											=		False
     _a											=		False

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		LlamaModelTester(self       )
           _A							:  Union[str, Any]    =		ConfigTester(self	,  config_class=_a	,  hidden_size=37       )

     def       a__			(						self       )		->							Union[str, Any]:
           self.config_tester.run_common_tests()

     def       a__			(						self       )		->							int:
           _A							:  Optional[int]    =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a       )

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 _A							:  Optional[Any]    =		type
                 self.model_tester.create_and_check_model(*_a       )

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

     def       a__			(						self       )		->							str:
           _A    ,     _A							:  List[str]    =		self.model_tester.prepare_config_and_inputs_for_common()
           _A							:  Tuple    =		3
           _A							:  Union[str, Any]    =		"""single_label_classification"""
           _A							:  List[str]    =		input_dict["""input_ids"""]
           _A							:  int    =		input_ids.ne(1       ).to(_a       )
           _A							:  Tuple    =		ids_tensor([self.model_tester.batch_size]	,  self.model_tester.type_sequence_label_size       )
           _A							:  Tuple    =		LlamaForSequenceClassification(_a       )
           model.to(_a       )
           model.eval()
           _A							:  Optional[Any]    =		model(_a	,  attention_mask=_a	,  labels=_a       )
           self.assertEqual(result.logits.shape	,  (self.model_tester.batch_size, self.model_tester.num_labels)       )

     def       a__			(						self       )		->							Optional[Any]:
           _A    ,     _A							:  List[Any]    =		self.model_tester.prepare_config_and_inputs_for_common()
           _A							:  Tuple    =		3
           _A							:  Dict    =		"""multi_label_classification"""
           _A							:  Optional[Any]    =		input_dict["""input_ids"""]
           _A							:  Any    =		input_ids.ne(1       ).to(_a       )
           _A							:  str    =		ids_tensor(
               [self.model_tester.batch_size, config.num_labels]	,  self.model_tester.type_sequence_label_size       ).to(torch.float       )
           _A							:  int    =		LlamaForSequenceClassification(_a       )
           model.to(_a       )
           model.eval()
           _A							:  Optional[int]    =		model(_a	,  attention_mask=_a	,  labels=_a       )
           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       )		->							Optional[int]:
           pass



     @parameterized.expand([("""linear""",), ("""dynamic""",)]       )
     def       a__			(						self	,  _a       )		->							Optional[Any]:
           _A    ,     _A							:  Dict    =		self.model_tester.prepare_config_and_inputs_for_common()
           _A							:  Tuple    =		ids_tensor([1, 10]	,  config.vocab_size       )
           _A							:  str    =		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							:  int    =		LlamaModel(_a       )
           original_model.to(_a       )
           original_model.eval()
           _A							:  List[str]    =		original_model(_a       ).last_hidden_state
           _A							:  str    =		original_model(_a       ).last_hidden_state

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

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







@require_torch
class 		lowercase							(     unittest.TestCase	):

     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""       )
     @slow
     def       a__			(						self       )		->							Tuple:
           _A							:  Optional[int]    =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
           _A							:  Tuple    =		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([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]       )
           torch.testing.assert_close(out.mean(-1       )	,  _a	,  atol=1e-2	,  rtol=1e-2       )
           # slicing logits[0, 0, 0:30]
           # fmt: off
           _A							:  str    =		torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]       )
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]	,  _a	,  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       )		->							List[str]:
           _A							:  int    =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
           _A							:  int    =		LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""	,  device_map="""auto"""       )
           _A							:  List[str]    =		model(torch.tensor(_a       )       )
           # Expected mean on dim = -1
           _A							:  str    =		torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]       )
           torch.testing.assert_close(out.mean(-1       )	,  _a	,  atol=1e-2	,  rtol=1e-2       )
           # slicing logits[0, 0, 0:30]
           # fmt: off
           _A							:  List[Any]    =		torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]       )
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]	,  _a	,  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       )		->							Dict:
           _A							:  Union[str, Any]    =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
           _A							:  List[Any]    =		LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""	,  device_map="""auto"""       )
           _A							:  Optional[Any]    =		model(torch.tensor(_a       )       )
           # Expected mean on dim = -1
           _A							:  Union[str, Any]    =		torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]       )
           torch.testing.assert_close(out.mean(-1       )	,  _a	,  atol=1e-2	,  rtol=1e-2       )
           # slicing logits[0, 0, 0:30]
           # fmt: off
           _A							:  List[Any]    =		torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]       )
           # fmt: on
           torch.testing.assert_close(out.mean(-1       )	,  _a	,  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       )		->							int:
           _A							:  List[str]    =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
           _A							:  Optional[Any]    =		LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""	,  device_map="""auto"""       )
           _A							:  Any    =		model(torch.tensor(_a       )       )

           _A							:  List[str]    =		torch.tensor(
               [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]	,  dtype=torch.floataa       )
           torch.testing.assert_close(out.mean(-1       )	,  _a	,  atol=1e-2	,  rtol=1e-2       )
           # fmt: off
           _A							:  Any    =		torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]       )
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]	,  _a	,  atol=1e-5	,  rtol=1e-5       )



     @unittest.skip("""Model is curently gated"""       )
     @slow
     def       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		"""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							:  Optional[int]    =		"""Simply put, the theory of relativity states that """
           _A							:  List[str]    =		LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""       )
           _A							:  Dict    =		tokenizer.encode(_a	,  return_tensors="""pt"""       )
           _A							:  Any    =		LlamaForCausalLM.from_pretrained(
               """meta-llama/Llama-2-13b-chat-hf"""	,  device_map="""sequential"""	,  use_safetensors=_a       )

           # greedy generation outputs
           _A							:  Optional[Any]    =		model.generate(_a	,  max_new_tokens=64	,  top_p=_a	,  temperature=1	,  do_sample=_a       )
           _A							:  List[Any]    =		tokenizer.decode(generated_ids[0]	,  skip_special_tokens=_a       )
           self.assertEqual(_a	,  _a       )







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

