
"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''camembert'''


						def __init__(			self  :   Optional[int]	,A_  :   Any=3_0522	,A_  :   Any=768	,A_  :   Tuple=12	,A_  :   Optional[int]=12	,A_  :   Union[str, Any]=3072	,A_  :   Dict="gelu"	,A_  :   Optional[Any]=0.1	,A_  :   Union[str, Any]=0.1	,A_  :   List[Any]=512	,A_  :   Tuple=2	,A_  :   Optional[int]=0.02	,A_  :   Optional[int]=1e-12	,A_  :   int=1	,A_  :   Tuple=0	,A_  :   List[Any]=2	,A_  :   Optional[int]="absolute"	,A_  :   Optional[Any]=True	,A_  :   Union[str, Any]=None	,**A_  :   Optional[int]	,)  -> List[str]:
							super().__init__(pad_token_id=A_	,bos_token_id=A_	,eos_token_id=A_	,**A_   )

							A     = vocab_size
							A     = hidden_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = hidden_act
							A     = intermediate_size
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = max_position_embeddings
							A     = type_vocab_size
							A     = initializer_range
							A     = layer_norm_eps
							A     = position_embedding_type
							A     = use_cache
							A     = classifier_dropout







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
								A     = {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
								A     = {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]   )

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


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

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

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

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

							A     = self.get_config()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Any:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Dict	,A_  :   Optional[int]	,A_  :   Any	,A_  :   Optional[Any]	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = LlamaModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_   )
							A     = model(A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   List[str]	,A_  :   Optional[int]	,A_  :   Any	,A_  :   str	,A_  :   Dict	,A_  :   Dict	,A_  :   Tuple	,A_  :   Tuple	,A_  :   Dict	,)  -> Union[str, Any]:
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Dict	,A_  :   Any	,A_  :   int	,A_  :   List[str]	,A_  :   Tuple	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   int	,)  -> List[Any]:
							A     = True
							A     = True
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()

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

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

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

							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,output_hidden_states=A_	,)['hidden_states'][0]
							A     = 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     = ids_tensor((1,)	,output_from_past.shape[-1]   ).item()
							A     = output_from_no_past[:, -3:, random_slice_idx].detach()
							A     = output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(A_	,A_	,atol=1e-3   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Optional[int]:
							A     = self.prepare_config_and_inputs()
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = config_and_inputs
							A     = {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							A     = LlamaModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,hidden_size=37   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[int]:
							A     = self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								A     = type
								self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = 'multi_label_classification'
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]	,self.model_tester.type_sequence_label_size   ).to(torch.float   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							pass




						@parameterized.expand([('linear',), ('dynamic',)]   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Any   )  -> str:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = ids_tensor([1, 10]	,config.vocab_size   )
							A     = ids_tensor([1, int(config.max_position_embeddings * 1.5   )]	,config.vocab_size   )

							set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
							A     = LlamaModel(A_   )
							original_model.to(A_   )
							original_model.eval()
							A     = original_model(A_   ).last_hidden_state
							A     = original_model(A_   ).last_hidden_state

							set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
							A     = {'type': scaling_type, 'factor': 10.0}
							A     = LlamaModel(A_   )
							scaled_model.to(A_   )
							scaled_model.eval()
							A     = scaled_model(A_   ).last_hidden_state
							A     = 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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'	,device_map='auto'   )
							A     = model(torch.tensor([input_ids]   )   )
							# Expected mean on dim = -1
							A     = torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]   )
							torch.testing.assert_close(out.mean(-1   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]   )
							# 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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )
							# Expected mean on dim = -1
							A     = torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]   )
							torch.testing.assert_close(out.mean(-1   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]   )
							# 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     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> str:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )
							# Expected mean on dim = -1
							A     = torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]   )
							torch.testing.assert_close(out.mean(-1   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]   )
							# 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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> str:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )

							A     = torch.tensor(
							    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]	,dtype=torch.floataa   )
							torch.testing.assert_close(out.mean(-1   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# fmt: off
							A     = torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]   )
							# 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     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = '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     = 'Simply put, the theory of relativity states that '
							A     = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   )
							A     = tokenizer.encode(A_	,return_tensors='pt'   )
							A     = LlamaForCausalLM.from_pretrained(
							    'meta-llama/Llama-2-13b-chat-hf'	,device_map='sequential'	,use_safetensors=A_   )

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