



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 SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A :       Union[str, Any]          =   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 :       str          =   ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size		)

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

        A :       Optional[int]          =   self.get_config()

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






    def 			_lowerCAmelCase					(       self		):
        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=lowerCamelCase__,		initializer_range=self.initializer_range,		)






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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		):
        A :       Any          =   True
        A :       Optional[Any]          =   LlamaModel(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A :       Optional[int]          =   model(
            lowerCamelCase__,		attention_mask=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		encoder_attention_mask=lowerCamelCase__,		)
        A :       Any          =   model(
            lowerCamelCase__,		attention_mask=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		)
        A :       List[Any]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)
        self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		):
        A :       Optional[int]          =   LlamaForCausalLM(config=lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A :       List[Any]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		labels=lowerCamelCase__		)
        self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.seq_length, self.vocab_size)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		):
        A :       Dict          =   True
        A :       Optional[int]          =   True
        A :       Union[str, Any]          =   LlamaForCausalLM(config=lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()

        # first forward pass
        A :       Optional[Any]          =   model(
            lowerCamelCase__,		attention_mask=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		encoder_attention_mask=lowerCamelCase__,		use_cache=lowerCamelCase__,		)
        A :       Dict          =   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 :       List[Any]          =   ids_tensor((self.batch_size, 3),		vocab_size=2		)

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

        A :       Dict          =   model(
            lowerCamelCase__,		attention_mask=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		encoder_attention_mask=lowerCamelCase__,		output_hidden_states=lowerCamelCase__,		)["""hidden_states"""][0]
        A :       str          =   model(
            lowerCamelCase__,		attention_mask=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		encoder_attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		output_hidden_states=lowerCamelCase__,		)["""hidden_states"""][0]

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






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self.prepare_config_and_inputs()
        (
            (
            A
        )     ,							(
            A
        )     ,							(
            A
        )     ,							(
            A
        )     ,							(
            A
        )     ,							(
            A
        )     ,							(
            A
        )     ,							
        ) :       int          =   config_and_inputs
        A :       Dict          =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   LlamaModelTester(self		)
        A :       Optional[Any]          =   ConfigTester(self,		config_class=lowerCamelCase__,		hidden_size=37		)






    def 			_lowerCAmelCase					(       self		):
        self.config_tester.run_common_tests()






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            A :       List[Any]          =   type
            self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()
        A :       int          =   3
        A :       Union[str, Any]          =   input_dict["""input_ids"""]
        A :       Optional[Any]          =   input_ids.ne(1		).to(lowerCamelCase__		)
        A :       Tuple          =   ids_tensor([self.model_tester.batch_size],		self.model_tester.type_sequence_label_size		)
        A :       Dict          =   LlamaForSequenceClassification(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A :       Optional[int]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		labels=lowerCamelCase__		)
        self.assertEqual(result.logits.shape,		(self.model_tester.batch_size, self.model_tester.num_labels)		)






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






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






    @parameterized.expand([("""linear""",), ("""dynamic""",)]		)
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()
        A :       List[str]          =   ids_tensor([1, 10],		config.vocab_size		)
        A :       Union[str, Any]          =   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 :       List[str]          =   LlamaModel(lowerCamelCase__		)
        original_model.to(lowerCamelCase__		)
        original_model.eval()
        A :       Optional[int]          =   original_model(lowerCamelCase__		).last_hidden_state
        A :       Union[str, Any]          =   original_model(lowerCamelCase__		).last_hidden_state

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

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






@require_torch
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""		)
    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       int          =   [1, 306, 4658, 278, 6593, 310, 2834, 338]
        A :       Dict          =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""",		device_map="""auto"""		)
        A :       int          =   model(torch.tensor([input_ids]		)		)
        # Expected mean on dim = -1
        A :       Dict          =   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		),		lowerCamelCase__,		atol=1e-2,		rtol=1e-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A :       Union[str, Any]          =   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],		lowerCamelCase__,		atol=1e-5,		rtol=1e-5		)






    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""		)
    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   [1, 306, 4658, 278, 6593, 310, 2834, 338]
        A :       List[Any]          =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""",		device_map="""auto"""		)
        A :       List[Any]          =   model(torch.tensor(lowerCamelCase__		)		)
        # Expected mean on dim = -1
        A :       Optional[int]          =   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		),		lowerCamelCase__,		atol=1e-2,		rtol=1e-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A :       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],		lowerCamelCase__,		atol=1e-5,		rtol=1e-5		)






    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""		)
    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   [1, 306, 4658, 278, 6593, 310, 2834, 338]
        A :       int          =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""",		device_map="""auto"""		)
        A :       int          =   model(torch.tensor(lowerCamelCase__		)		)
        # Expected mean on dim = -1
        A :       int          =   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		),		lowerCamelCase__,		atol=1e-2,		rtol=1e-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A :       Optional[int]          =   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		),		lowerCamelCase__,		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 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   [1, 306, 4658, 278, 6593, 310, 2834, 338]
        A :       Optional[int]          =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""",		device_map="""auto"""		)
        A :       List[Any]          =   model(torch.tensor(lowerCamelCase__		)		)

        A :       List[Any]          =   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		),		lowerCamelCase__,		atol=1e-2,		rtol=1e-2		)
        # fmt: off
        A :       Optional[int]          =   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],		lowerCamelCase__,		atol=1e-5,		rtol=1e-5		)






    @unittest.skip("""Model is curently gated"""		)
    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   """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 :       Dict          =   LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""		)
        A :       Optional[Any]          =   tokenizer.encode(lowerCamelCase__,		return_tensors="""pt"""		)
        A :       List[str]          =   LlamaForCausalLM.from_pretrained(
            """meta-llama/Llama-2-13b-chat-hf""",		device_map="""sequential""",		use_safetensors=lowerCamelCase__		)

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







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


