




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	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=True	,    _A=False	,    _A=True	,    _A=9_9	,    _A=3_2	,    _A=5	,    _A=4	,    _A=3_7	,    _A="gelu"	,    _A=0.1	,    _A=0.1	,    _A=5_1_2	,    _A=1_6	,    _A=2	,    _A=0.02	,    _A=3	,    _A=4	,    _A=None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_input_mask
											__lowerCAmelCase            = use_token_type_ids
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_act
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = type_vocab_size
											__lowerCAmelCase            = type_sequence_label_size
											__lowerCAmelCase            = initializer_range
											__lowerCAmelCase            = num_labels
											__lowerCAmelCase            = num_choices
											__lowerCAmelCase            = scope






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size    )

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

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

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

											__lowerCAmelCase            = self.get_config()

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return LlamaConfig(
											    vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=_A	,    initializer_range=self.initializer_range	,    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = LlamaModel(config=_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = model(_A	,    attention_mask=_A    )
											__lowerCAmelCase            = model(_A    )
											self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = True
											__lowerCAmelCase            = LlamaModel(_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = model(
											    _A	,    attention_mask=_A	,    encoder_hidden_states=_A	,    encoder_attention_mask=_A	,    )
											__lowerCAmelCase            = model(
											    _A	,    attention_mask=_A	,    encoder_hidden_states=_A	,    )
											__lowerCAmelCase            = 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	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = LlamaForCausalLM(config=_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = 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	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = True
											__lowerCAmelCase            = True
											__lowerCAmelCase            = LlamaForCausalLM(config=_A    )
											model.to(_A    )
											model.eval()

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

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

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

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




											"""simple docstring"""



											__lowerCAmelCase            = self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,							(
											    __lowerCAmelCase							
											)     ,														
											)            = config_and_inputs
											__lowerCAmelCase            = {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_torch
class     a__					(      snake_case__				,       snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Any      =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						_a							:					Dict      =					(LlamaForCausalLM,) if is_torch_available() else ()
						_a							:					List[str]      =					(
						    {
						        """feature-extraction""": LlamaModel,
						        """text-classification""": LlamaForSequenceClassification,
						        """text-generation""": LlamaForCausalLM,
						        """zero-shot""": LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_a							:					List[Any]      =					False
						_a							:					List[str]      =					False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = LlamaModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A	,    hidden_size=3_7    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																__lowerCAmelCase            = type
																self.model_tester.create_and_check_model(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase            = 3
											__lowerCAmelCase            = input_dict["input_ids"]
											__lowerCAmelCase            = input_ids.ne(1    ).to(_A    )
											__lowerCAmelCase            = ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size    )
											__lowerCAmelCase            = LlamaForSequenceClassification(_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = 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    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase            = 3
											__lowerCAmelCase            = "single_label_classification"
											__lowerCAmelCase            = input_dict["input_ids"]
											__lowerCAmelCase            = input_ids.ne(1    ).to(_A    )
											__lowerCAmelCase            = ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size    )
											__lowerCAmelCase            = LlamaForSequenceClassification(_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = 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    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase            = 3
											__lowerCAmelCase            = "multi_label_classification"
											__lowerCAmelCase            = input_dict["input_ids"]
											__lowerCAmelCase            = input_ids.ne(1    ).to(_A    )
											__lowerCAmelCase            = ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]	,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
											__lowerCAmelCase            = LlamaForSequenceClassification(_A    )
											model.to(_A    )
											model.eval()
											__lowerCAmelCase            = 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    ):




											"""simple docstring"""



											pass






						@parameterized.expand([("linear",), ("dynamic",)]    )
						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase            = ids_tensor([1, 1_0]	,    config.vocab_size    )
											__lowerCAmelCase            = ids_tensor([1, int(config.max_position_embeddings * 1.5    )]	,    config.vocab_size    )

											set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
											__lowerCAmelCase            = LlamaModel(_A    )
											original_model.to(_A    )
											original_model.eval()
											__lowerCAmelCase            = original_model(_A    ).last_hidden_state
											__lowerCAmelCase            = original_model(_A    ).last_hidden_state

											set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
											__lowerCAmelCase            = {"type": scaling_type, "factor": 10.0}
											__lowerCAmelCase            = LlamaModel(_A    )
											scaled_model.to(_A    )
											scaled_model.eval()
											__lowerCAmelCase            = scaled_model(_A    ).last_hidden_state
											__lowerCAmelCase            = 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     a__					(      unittest.TestCase   ):






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




											"""simple docstring"""



											__lowerCAmelCase            = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
											__lowerCAmelCase            = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"	,    device_map="auto"    )
											__lowerCAmelCase            = model(torch.tensor([input_ids]    )    )
											# Expected mean on dim = -1
											__lowerCAmelCase            = 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
											__lowerCAmelCase            = 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, :3_0]	,    _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    ):




											"""simple docstring"""



											__lowerCAmelCase            = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
											__lowerCAmelCase            = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"	,    device_map="auto"    )
											__lowerCAmelCase            = model(torch.tensor(_A    )    )
											# Expected mean on dim = -1
											__lowerCAmelCase            = 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
											__lowerCAmelCase            = 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, :3_0]	,    _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    ):




											"""simple docstring"""



											__lowerCAmelCase            = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
											__lowerCAmelCase            = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"	,    device_map="auto"    )
											__lowerCAmelCase            = model(torch.tensor(_A    )    )
											# Expected mean on dim = -1
											__lowerCAmelCase            = 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
											__lowerCAmelCase            = 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    ):




											"""simple docstring"""



											__lowerCAmelCase            = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
											__lowerCAmelCase            = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"	,    device_map="auto"    )
											__lowerCAmelCase            = model(torch.tensor(_A    )    )

											__lowerCAmelCase            = 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
											__lowerCAmelCase            = 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, :3_0]	,    _A	,    atol=1E-5	,    rtol=1E-5    )






						@unittest.skip("Model is curently gated"    )
						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "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"
											__lowerCAmelCase            = "Simply put, the theory of relativity states that "
											__lowerCAmelCase            = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"    )
											__lowerCAmelCase            = tokenizer.encode(_A	,    return_tensors="pt"    )
											__lowerCAmelCase            = LlamaForCausalLM.from_pretrained(
											    "meta-llama/Llama-2-13b-chat-hf"	,    device_map="sequential"	,    use_safetensors=_A    )

											# greedy generation outputs
											__lowerCAmelCase            = model.generate(_A	,    max_new_tokens=6_4	,    top_p=_A	,    temperature=1	,    do_sample=_A    )
											__lowerCAmelCase            = tokenizer.decode(generated_ids[0]	,    skip_special_tokens=_A    )
											self.assertEqual(_A	,    _A    )










import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class     a__					(      snake_case__   ):






						def __init__(     self	,    _A = "▁"	,    _A = True	,    _A = "<unk>"	,    _A = "</s>"	,    _A = "<pad>"	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = {
											    "pad": {"id": 0, "token": pad_token},
											    "eos": {"id": 1, "token": eos_token},
											    "unk": {"id": 2, "token": unk_token},
											}

											__lowerCAmelCase            = [None] * len(self.special_tokens    )
											for token_dict in self.special_tokens.values():
																__lowerCAmelCase            = token_dict["token"]

											__lowerCAmelCase            = Tokenizer(Unigram()    )

											__lowerCAmelCase            = normalizers.Sequence(
											    [
											        normalizers.Nmt(),
											        normalizers.NFKC(),
											        normalizers.Replace(Regex(" {2,}"    )	,    " "    ),
											        normalizers.Lowercase(),
											    ]    )
											__lowerCAmelCase            = pre_tokenizers.Sequence(
											    [
											        pre_tokenizers.Metaspace(replacement=_A	,    add_prefix_space=_A    ),
											        pre_tokenizers.Digits(individual_digits=_A    ),
											        pre_tokenizers.Punctuation(),
											    ]    )
											__lowerCAmelCase            = decoders.Metaspace(replacement=_A	,    add_prefix_space=_A    )

											__lowerCAmelCase            = TemplateProcessing(
											    single=f"""$A {self.special_tokens['eos']['token']}"""	,    special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]	,    )

											__lowerCAmelCase            = {
											    "model": "SentencePieceUnigram",
											    "replacement": replacement,
											    "add_prefix_space": add_prefix_space,
											}

											super().__init__(_A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											if isinstance(_A	,    _A    ):
																__lowerCAmelCase            = [files]
											self._tokenizer.train(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											self._tokenizer.train_from_iterator(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = json.loads(self._tokenizer.to_str()    )

											__lowerCAmelCase            = self.special_tokens["unk"]["id"]

											__lowerCAmelCase            = Tokenizer.from_str(json.dumps(_A    )    )




