

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormModel,
       )


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=True						,       a=True						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=4						,       ):
         lowercase__   :       Any				=	parent
         lowercase__   :       Union[str, Any]				=	batch_size
         lowercase__   :       str				=	seq_length
         lowercase__   :       Union[str, Any]				=	is_training
         lowercase__   :       Optional[int]				=	use_attention_mask
         lowercase__   :       Any				=	use_token_type_ids
         lowercase__   :       Any				=	use_labels
         lowercase__   :       Optional[Any]				=	vocab_size
         lowercase__   :       int				=	hidden_size
         lowercase__   :       Optional[Any]				=	num_hidden_layers
         lowercase__   :       str				=	num_attention_heads
         lowercase__   :       int				=	intermediate_size
         lowercase__   :       Optional[Any]				=	hidden_act
         lowercase__   :       List[Any]				=	hidden_dropout_prob
         lowercase__   :       Dict				=	attention_probs_dropout_prob
         lowercase__   :       Optional[Any]				=	max_position_embeddings
         lowercase__   :       Any				=	type_vocab_size
         lowercase__   :       List[Any]				=	type_sequence_label_size
         lowercase__   :       Optional[Any]				=	initializer_range
         lowercase__   :       str				=	num_choices





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       str				=	None
         if self.use_attention_mask:
              lowercase__   :       List[str]				=	random_attention_mask([self.batch_size, self.seq_length])

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

         lowercase__   :       str				=	RobertaPreLayerNormConfig(
             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						,       )

         return config, input_ids, token_type_ids, attention_mask





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.prepare_config_and_inputs()
         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Optional[int]				=	config_and_inputs
         lowercase__   :       List[Any]				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	self.prepare_config_and_inputs()
         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Optional[Any]				=	config_and_inputs

         lowercase__   :       Dict				=	True
         lowercase__   :       Optional[int]				=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase__   :       Any				=	ids_tensor([self.batch_size, self.seq_length]						,       vocab_size=2)

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					True

    __lowerCamelCase						:	Union[str, Any]														=					(
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	FlaxRobertaPreLayerNormModelTester(self)





    @slow
    def 				snake_case_    (				self):
         for model_class_name in self.all_model_classes:
              lowercase__   :       Any				=	model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,       from_pt=a)
              lowercase__   :       List[Any]				=	model(np.ones((1, 1)))
              self.assertIsNotNone(a)



@require_flax
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,       from_pt=a)

         lowercase__   :       Tuple				=	np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,       dtype=jnp.intaa)
         lowercase__   :       Optional[Any]				=	model(a)[0]
         lowercase__   :       Any				=	[1, 11, 5_0265]
         self.assertEqual(list(output.shape)						,       a)
         # compare the actual values for a slice.
         lowercase__   :       Tuple				=	np.array(
             [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]						,       dtype=np.floataa)
         self.assertTrue(np.allclose(output[:, :3, :3]						,       a						,       atol=1e-4))





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,       from_pt=a)

         lowercase__   :       Dict				=	np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,       dtype=jnp.intaa)
         lowercase__   :       List[str]				=	model(a)[0]
         # compare the actual values for a slice.
         lowercase__   :       Optional[int]				=	np.array(
             [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]						,       dtype=np.floataa)
         self.assertTrue(np.allclose(output[:, :3, :3]						,       a						,       atol=1e-4))




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

