





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 					A     :



      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_=0.02  ,	lowercase_=3  ,	lowercase_=4  ,	lowercase_=None  ,	)   -> Dict:



             '''simple docstring'''

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



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

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

             _snake_case   :				int  					=    None
             if self.use_input_mask:
                    _snake_case   :				Tuple  					=    random_attention_mask([self.batch_size, self.seq_length]       )

             _snake_case   :				List[Any]  					=    None
             if self.use_token_type_ids:
                    _snake_case   :				Union[str, Any]  					=    ids_tensor([self.batch_size, self.seq_length]  ,	self.type_vocab_size       )

             _snake_case   :				Optional[Any]  					=    None
             _snake_case   :				Dict  					=    None
             _snake_case   :				Union[str, Any]  					=    None
             if self.use_labels:
                    _snake_case   :				Tuple  					=    ids_tensor([self.batch_size]  ,	self.type_sequence_label_size       )
                    _snake_case   :				Any  					=    ids_tensor([self.batch_size, self.seq_length]  ,	self.num_labels       )
                    _snake_case   :				Any  					=    ids_tensor([self.batch_size]  ,	self.num_choices       )

             _snake_case   :				Any  					=    self.get_config()

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



      def  __a			(       self       )   -> Tuple:



             '''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  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       )   -> Optional[int]:



             '''simple docstring'''

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



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	)   -> Union[str, Any]:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    True
             _snake_case   :				Any  					=    LlamaModel(lowercase_       )
             model.to(lowercase_       )
             model.eval()
             _snake_case   :				List[Any]  					=    model(
                 lowercase_  ,	attention_mask=lowercase_  ,	encoder_hidden_states=lowercase_  ,	encoder_attention_mask=lowercase_  ,	)
             _snake_case   :				Dict  					=    model(
                 lowercase_  ,	attention_mask=lowercase_  ,	encoder_hidden_states=lowercase_  ,	)
             _snake_case   :				List[str]  					=    model(lowercase_  ,	attention_mask=lowercase_       )
             self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size)       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	)   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				Dict  					=    LlamaForCausalLM(config=lowercase_       )
             model.to(lowercase_       )
             model.eval()
             _snake_case   :				Any  					=    model(lowercase_  ,	attention_mask=lowercase_  ,	labels=lowercase_       )
             self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	)   -> Dict:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    True
             _snake_case   :				Optional[int]  					=    True
             _snake_case   :				str  					=    LlamaForCausalLM(config=lowercase_       )
             model.to(lowercase_       )
             model.eval()

             # first forward pass
             _snake_case   :				List[str]  					=    model(
                 lowercase_  ,	attention_mask=lowercase_  ,	encoder_hidden_states=lowercase_  ,	encoder_attention_mask=lowercase_  ,	use_cache=lowercase_  ,	)
             _snake_case   :				Any  					=    outputs.past_key_values

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

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

             _snake_case   :				Dict  					=    model(
                 lowercase_  ,	attention_mask=lowercase_  ,	encoder_hidden_states=lowercase_  ,	encoder_attention_mask=lowercase_  ,	output_hidden_states=lowercase_  ,	)['''hidden_states'''][0]
             _snake_case   :				List[str]  					=    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
             _snake_case   :				int  					=    ids_tensor((1,)  ,	output_from_past.shape[-1]       ).item()
             _snake_case   :				str  					=    output_from_no_past[:, -3:, random_slice_idx].detach()
             _snake_case   :				Optional[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(lowercase_  ,	lowercase_  ,	atol=1E-3       )       )



      def  __a			(       self       )   -> str:



             '''simple docstring'''

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


@require_torch
class 					A     (_snake_case							,_snake_case							,_snake_case							,unittest.TestCase					):
      _SCREAMING_SNAKE_CASE       =				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE       =				(LlamaForCausalLM,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE       =				(
          {
              'feature-extraction': LlamaModel,
              'text-classification': LlamaForSequenceClassification,
              'text-generation': LlamaForCausalLM,
              'zero-shot': LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE       =				False
      _SCREAMING_SNAKE_CASE       =				False



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				str  					=    LlamaModelTester(self       )
             _snake_case   :				Any  					=    ConfigTester(self  ,	config_class=lowercase_  ,	hidden_size=37       )



      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

             self.config_tester.run_common_tests()



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

             _snake_case   :				int  					=    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_       )



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _snake_case   :				Any  					=    type
                    self.model_tester.create_and_check_model(*lowercase_       )



      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

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



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

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



      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

             _snake_case					,     _snake_case   :				Any  					=    self.model_tester.prepare_config_and_inputs_for_common()
             _snake_case   :				str  					=    3
             _snake_case   :				int  					=    '''multi_label_classification'''
             _snake_case   :				Any  					=    input_dict['''input_ids''']
             _snake_case   :				Optional[Any]  					=    input_ids.ne(1       ).to(lowercase_       )
             _snake_case   :				int  					=    ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]  ,	self.model_tester.type_sequence_label_size       ).to(torch.float       )
             _snake_case   :				Optional[int]  					=    LlamaForSequenceClassification(lowercase_       )
             model.to(lowercase_       )
             model.eval()
             _snake_case   :				List[str]  					=    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  __a			(       self       )   -> int:



             '''simple docstring'''

             pass



      @parameterized.expand([('''linear''',), ('''dynamic''',)]       )
      def  __a			(       self  ,	lowercase_       )   -> Union[str, Any]:



             '''simple docstring'''

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

             set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
             _snake_case   :				Optional[Any]  					=    {'''type''': scaling_type, '''factor''': 10.0}
             _snake_case   :				int  					=    LlamaModel(lowercase_       )
             scaled_model.to(lowercase_       )
             scaled_model.eval()
             _snake_case   :				str  					=    scaled_model(lowercase_       ).last_hidden_state
             _snake_case   :				Union[str, 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 					A     (unittest.TestCase					):



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



             '''simple docstring'''

             _snake_case   :				int  					=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
             _snake_case   :				Dict  					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''  ,	device_map='''auto'''       )
             _snake_case   :				str  					=    model(torch.tensor([input_ids]       )       )
             # Expected mean on dim = -1
             _snake_case   :				List[Any]  					=    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       )  ,	lowercase_  ,	atol=1E-2  ,	rtol=1E-2       )
             # slicing logits[0, 0, 0:30]
             # fmt: off
             _snake_case   :				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]  ,	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  __a			(       self       )   -> Dict:



             '''simple docstring'''

             _snake_case   :				Dict  					=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
             _snake_case   :				str  					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''  ,	device_map='''auto'''       )
             _snake_case   :				List[str]  					=    model(torch.tensor(lowercase_       )       )
             # Expected mean on dim = -1
             _snake_case   :				List[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       )  ,	lowercase_  ,	atol=1E-2  ,	rtol=1E-2       )
             # slicing logits[0, 0, 0:30]
             # fmt: off
             _snake_case   :				int  					=    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]  ,	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  __a			(       self       )   -> Dict:



             '''simple docstring'''

             _snake_case   :				str  					=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
             _snake_case   :				int  					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  ,	device_map='''auto'''       )
             _snake_case   :				Dict  					=    model(torch.tensor(lowercase_       )       )
             # Expected mean on dim = -1
             _snake_case   :				Tuple  					=    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       )  ,	lowercase_  ,	atol=1E-2  ,	rtol=1E-2       )
             # slicing logits[0, 0, 0:30]
             # fmt: off
             _snake_case   :				Tuple  					=    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       )  ,	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  __a			(       self       )   -> Any:



             '''simple docstring'''

             _snake_case   :				Tuple  					=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
             _snake_case   :				Optional[int]  					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''  ,	device_map='''auto'''       )
             _snake_case   :				List[str]  					=    model(torch.tensor(lowercase_       )       )

             _snake_case   :				Dict  					=    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       )  ,	lowercase_  ,	atol=1E-2  ,	rtol=1E-2       )
             # fmt: off
             _snake_case   :				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]  ,	lowercase_  ,	atol=1E-5  ,	rtol=1E-5       )



      @unittest.skip('''Model is curently gated'''       )
      @slow
      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				Any  					=    '''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'''
             _snake_case   :				int  					=    '''Simply put, the theory of relativity states that '''
             _snake_case   :				Optional[int]  					=    LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''       )
             _snake_case   :				Dict  					=    tokenizer.encode(lowercase_  ,	return_tensors='''pt'''       )
             _snake_case   :				Union[str, Any]  					=    LlamaForCausalLM.from_pretrained(
                 '''meta-llama/Llama-2-13b-chat-hf'''  ,	device_map='''sequential'''  ,	use_safetensors=lowercase_       )

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













'''simple docstring'''

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   import os as original_os
   from os import path as original_path
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_mock__"""
   with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
      # Every way to access os.path.join must be patched, and the rest must stay untouched

      # check os.path.join
      assert isinstance(_test_patching.os				,						_PatchedModuleObj   )
      assert isinstance(_test_patching.os.path				,						_PatchedModuleObj   )
      assert _test_patching.os.path.join is mock

      # check path.join
      assert isinstance(_test_patching.path				,						_PatchedModuleObj   )
      assert _test_patching.path.join is mock

      # check join
      assert _test_patching.join is mock

      # check that the other attributes are untouched
      assert _test_patching.os.rename is original_rename
      assert _test_patching.path.dirname is original_dirname
      assert _test_patching.os.path.dirname is original_dirname

      # Even renamed modules or objects must be patched

      # check renamed_os.path.join
      assert isinstance(_test_patching.renamed_os				,						_PatchedModuleObj   )
      assert isinstance(_test_patching.renamed_os.path				,						_PatchedModuleObj   )
      assert _test_patching.renamed_os.path.join is mock

      # check renamed_path.join
      assert isinstance(_test_patching.renamed_path				,						_PatchedModuleObj   )
      assert _test_patching.renamed_path.join is mock

      # check renamed_join
      assert _test_patching.renamed_join is mock

      # check that the other attributes are untouched
      assert _test_patching.renamed_os.rename is original_rename
      assert _test_patching.renamed_path.dirname is original_dirname
      assert _test_patching.renamed_os.path.dirname is original_dirname

   # check that everthing is back to normal when the patch is over

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   assert _test_patching.open is open

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_builtin_mock__"""
   # _test_patching has "open" in its globals
   assert _test_patching.open is open
   with patch_submodule(_test_patching				,						"""open"""				,						__UpperCAmelCase   ):
      assert _test_patching.open is mock

   # check that everthing is back to normal when the patch is over

   assert _test_patching.open is open

def     __magic_name__							(				)    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_missing_mock__"""
   with patch_submodule(_test_patching				,						"""pandas.read_csv"""				,						__UpperCAmelCase   ):
      pass

def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_missing_builtin_mock__"""
   # _test_patching doesn't have "len" in its globals
   assert getattr(_test_patching				,						"""len"""				,						__UpperCAmelCase   ) is None
   with patch_submodule(_test_patching				,						"""len"""				,						__UpperCAmelCase   ):
      assert _test_patching.len is mock
   assert _test_patching.len is len

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_start_and_stop_mock__"""
   __SCREAMING_SNAKE_CASE   				=							patch_submodule(_test_patching				,						"""open"""				,						__UpperCAmelCase   )
   assert _test_patching.open is open
   patch.start()
   assert _test_patching.open is mock
   patch.stop()
   assert _test_patching.open is open

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_join__"""
   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_dirname__"""
   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_rename__"""
   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename

   with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
      with patch_submodule(_test_patching				,						"""os.rename"""				,						__UpperCAmelCase   ):
         with patch_submodule(_test_patching				,						"""os.path.dirname"""				,						__UpperCAmelCase   ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

    # try another order
   with patch_submodule(_test_patching				,						"""os.rename"""				,						__UpperCAmelCase   ):
      with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
         with patch_submodule(_test_patching				,						"""os.path.dirname"""				,						__UpperCAmelCase   ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename







def     __magic_name__							(				)    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_doesnt_exist_mock__"""
   with patch_submodule(_test_patching				,						"""__module_that_doesn_exist__.__attribute_that_doesn_exist__"""				,						__UpperCAmelCase   ):
      pass
   with patch_submodule(_test_patching				,						"""os.__attribute_that_doesn_exist__"""				,						__UpperCAmelCase   ):
      pass





