



'''simple docstring'''




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class    lowerCamelCase__				:






      """simple docstring"""




      def __init__(       self							:     int			,a__							:     Dict			,a__							:     int=2			,a__							:     List[Any]=8			,a__							:     Any=True			,a__							:     Optional[Any]=True			,a__							:     Optional[Any]=True			,a__							:     int=True			,a__							:     int=99			,a__							:     Union[str, Any]=16			,a__							:     Optional[int]=5			,a__							:     Tuple=2			,a__							:     Dict=36			,a__							:     Optional[int]="gelu"			,a__							:     Optional[Any]=0.0			,a__							:     int=0.0			,a__							:     Tuple=5_12			,a__							:     Dict=16			,a__							:     int=2			,a__							:     Tuple=0.02			,a__							:     Any=3			,a__							:     Optional[Any]=4			,a__							:     Dict=None			,):
             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 lowerCAmelCase_					(       self							:     Union[str, 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 lowerCAmelCase_					(       self							:     int   ):
             return MraConfig(
                 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 lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.get_config()
             a__								= 3_00
             return config




      def lowerCAmelCase_					(       self							:     str   ):
             (
                 (
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					  
             )								= self.prepare_config_and_inputs()

             a__								= True
             a__								= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
             a__								= ids_tensor([self.batch_size, self.seq_length]			,vocab_size=2   )

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
                 choice_labels,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )




      def lowerCAmelCase_					(       self							:     Union[str, Any]			,a__							:     Union[str, Any]			,a__							:     Union[str, Any]			,a__							:     List[str]			,a__							:     List[Any]			,a__							:     str			,a__							:     List[str]			,a__							:     str   ):
             a__								= MraModel(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase   )
             a__								= model(__lowercase			,token_type_ids=__lowercase   )
             a__								= model(__lowercase   )
             self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)   )




      def lowerCAmelCase_					(       self							:     Optional[Any]			,a__							:     Optional[int]			,a__							:     List[Any]			,a__							:     Optional[int]			,a__							:     List[str]			,a__							:     Dict			,a__							:     List[str]			,a__							:     str			,a__							:     Any			,a__							:     List[str]			,):
             a__								= True
             a__								= MraModel(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,encoder_hidden_states=__lowercase			,encoder_attention_mask=__lowercase			,)
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,encoder_hidden_states=__lowercase			,)
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase   )
             self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)   )




      def lowerCAmelCase_					(       self							:     Union[str, Any]			,a__							:     List[Any]			,a__							:     Dict			,a__							:     List[Any]			,a__							:     Optional[Any]			,a__							:     str			,a__							:     Optional[Any]			,a__							:     Optional[Any]   ):
             a__								= MraForMaskedLM(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.vocab_size)   )




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     Dict			,a__							:     Optional[int]			,a__							:     int			,a__							:     Tuple			,a__							:     Tuple			,a__							:     Optional[Any]			,a__							:     str   ):
             a__								= MraForQuestionAnswering(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,start_positions=__lowercase			,end_positions=__lowercase			,)
             self.parent.assertEqual(result.start_logits.shape			,(self.batch_size, self.seq_length)   )
             self.parent.assertEqual(result.end_logits.shape			,(self.batch_size, self.seq_length)   )




      def lowerCAmelCase_					(       self							:     Any			,a__							:     List[Any]			,a__							:     str			,a__							:     List[Any]			,a__							:     Tuple			,a__							:     int			,a__							:     Any			,a__							:     Any   ):
             a__								= self.num_labels
             a__								= MraForSequenceClassification(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)   )




      def lowerCAmelCase_					(       self							:     Dict			,a__							:     Any			,a__							:     Optional[int]			,a__							:     Union[str, Any]			,a__							:     Tuple			,a__							:     Dict			,a__							:     int			,a__							:     Union[str, Any]   ):
             a__								= self.num_labels
             a__								= MraForTokenClassification(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= model(__lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.num_labels)   )




      def lowerCAmelCase_					(       self							:     List[Any]			,a__							:     int			,a__							:     List[str]			,a__							:     str			,a__							:     Optional[Any]			,a__							:     int			,a__							:     List[Any]			,a__							:     Tuple   ):
             a__								= self.num_choices
             a__								= MraForMultipleChoice(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             a__								= input_ids.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= token_type_ids.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= input_mask.unsqueeze(1   ).expand(-1			,self.num_choices			,-1   ).contiguous()
             a__								= model(
                 __lowercase			,attention_mask=__lowercase			,token_type_ids=__lowercase			,labels=__lowercase			,)
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_choices)   )




      def lowerCAmelCase_					(       self							:     str   ):
             a__								= self.prepare_config_and_inputs()
             (
                 (
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					(
                 a__  
             )						,					  
             )								= config_and_inputs
             a__								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict



@require_torch
class    lowerCamelCase__				(			__lowerCamelCase	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     (
          (
              MraModel,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase__			 =     False
      UpperCamelCase__			 =     False
      UpperCamelCase__			 =     False
      UpperCamelCase__			 =     False

      UpperCamelCase__			 =     ()




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= MraModelTester(self   )
             a__								= ConfigTester(self			,config_class=__lowercase			,hidden_size=37   )




      def lowerCAmelCase_					(       self							:     Dict   ):
             self.config_tester.run_common_tests()




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowercase   )




      def lowerCAmelCase_					(       self							:     str   ):
             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(*__lowercase   )




      def lowerCAmelCase_					(       self							:     int   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowercase   )




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowercase   )




      @slow
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):







             for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a__								= MraModel.from_pretrained(__lowercase   )
                    self.assertIsNotNone(__lowercase   )




      @unittest.skip(reason="MRA does not output attentions"   )
      def lowerCAmelCase_					(       self							:     str   ):
             return



@require_torch
class    lowerCamelCase__				(			unittest.TestCase						):






      """simple docstring"""




      @slow
      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= MraModel.from_pretrained("uw-madison/mra-base-512-4"   )
             a__								= torch.arange(2_56   ).unsqueeze(0   )

             with torch.no_grad():
                    a__								= model(__lowercase   )[0]

             a__								= torch.Size((1, 2_56, 7_68)   )
             self.assertEqual(output.shape			,__lowercase   )

             a__								= torch.tensor(
                 [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]   )

             self.assertTrue(torch.allclose(output[:, :3, :3]			,__lowercase			,atol=1e-4   )   )




      @slow
      def lowerCAmelCase_					(       self							:     str   ):
             a__								= MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"   )
             a__								= torch.arange(2_56   ).unsqueeze(0   )

             with torch.no_grad():
                    a__								= model(__lowercase   )[0]

             a__								= 5_02_65

             a__								= torch.Size((1, 2_56, vocab_size)   )
             self.assertEqual(output.shape			,__lowercase   )

             a__								= torch.tensor(
                 [[[9.2595, -3.6038, 11.88_19], [9.3869, -3.2693, 11.09_56], [11.85_24, -3.4938, 13.12_10]]]   )

             self.assertTrue(torch.allclose(output[:, :3, :3]			,__lowercase			,atol=1e-4   )   )




      @slow
      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"   )
             a__								= torch.arange(40_96   ).unsqueeze(0   )

             with torch.no_grad():
                    a__								= model(__lowercase   )[0]

             a__								= 5_02_65

             a__								= torch.Size((1, 40_96, vocab_size)   )
             self.assertEqual(output.shape			,__lowercase   )

             a__								= torch.tensor(
                 [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]   )

             self.assertTrue(torch.allclose(output[:, :3, :3]			,__lowercase			,atol=1e-4   )   )



'''simple docstring'''

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


UpperCAmelCase					 =	logging.get_logger(__name__)

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

UpperCAmelCase					 =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
            if pre_tok_state.get('add_prefix_space'    ,   __lowercase   ) != add_prefix_space:
                 __lowercase      		=getattr(__lowercase    ,   pre_tok_state.pop('type'   )   )
                 __lowercase      		=add_prefix_space
                 __lowercase      		=pre_tok_class(**__lowercase   )

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







            __lowercase      		=self._tokenizer.model.save(__lowercase    ,   name=__lowercase   )
            return tuple(__lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

            if len(__lowercase   ) > self.model_max_length:
                 __lowercase      		=input_ids[-self.model_max_length :]
            return input_ids


