



"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       Optional[int]        =						"gptj"
  snake_case__    :       Union[str, Any]        =						{
      "max_position_embeddings": "n_positions",
      "hidden_size": "n_embd",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }




  def __init__(			self					:						str						, __lowerCAmelCase					:						str=5_0_4_0_0						, __lowerCAmelCase					:						Optional[int]=2_0_4_8						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Dict=2_8						, __lowerCAmelCase					:						Any=1_6						, __lowerCAmelCase					:						Optional[Any]=6_4						, __lowerCAmelCase					:						Optional[int]=None						, __lowerCAmelCase					:						Any="gelu_new"						, __lowerCAmelCase					:						List[str]=0.0						, __lowerCAmelCase					:						Optional[int]=0.0						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[str]=1E-5						, __lowerCAmelCase					:						List[str]=0.02						, __lowerCAmelCase					:						Optional[int]=True						, __lowerCAmelCase					:						int=5_0_2_5_6						, __lowerCAmelCase					:						int=5_0_2_5_6						, __lowerCAmelCase					:						Union[str, Any]=False						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			vocab_size
         _lowerCamelCase						:    Union[str, Any]         =			n_positions
         _lowerCamelCase						:    int         =			n_embd
         _lowerCamelCase						:    Tuple         =			n_layer
         _lowerCamelCase						:    Any         =			n_head
         _lowerCamelCase						:    Optional[Any]         =			n_inner
         _lowerCamelCase						:    Union[str, Any]         =			rotary_dim
         _lowerCamelCase						:    List[Any]         =			activation_function
         _lowerCamelCase						:    Any         =			resid_pdrop
         _lowerCamelCase						:    List[str]         =			embd_pdrop
         _lowerCamelCase						:    Dict         =			attn_pdrop
         _lowerCamelCase						:    Any         =			layer_norm_epsilon
         _lowerCamelCase						:    List[Any]         =			initializer_range
         _lowerCamelCase						:    Union[str, Any]         =			use_cache

         _lowerCamelCase						:    Optional[int]         =			bos_token_id
         _lowerCamelCase						:    Any         =			eos_token_id

         super().__init__(
             bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, tie_word_embeddings=__lowerCAmelCase						, **__lowerCAmelCase		)






class 	__snake_case	(     _lowercase):




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						PretrainedConfig						, __lowerCAmelCase					:						str = "default"						, __lowerCAmelCase					:						List[PatchingSpec] = None						, __lowerCAmelCase					:						bool = False						, ):






         """simple docstring"""






         super().__init__(__lowerCAmelCase						, task=__lowerCAmelCase						, patching_specs=__lowerCAmelCase						, use_past=__lowerCAmelCase		)
         if not getattr(self._config						, '''pad_token_id'''						, __lowerCAmelCase		):
                # TODO: how to do that better?
                _lowerCamelCase						:    Tuple         =			0




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
         if self.use_past:
                self.fill_with_past_key_values_(__lowerCAmelCase						, direction='''inputs'''		)
                _lowerCamelCase						:    Union[str, Any]         =			{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
                _lowerCamelCase						:    str         =			{0: '''batch''', 1: '''sequence'''}

         return common_inputs




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         return self._config.n_layer




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         return self._config.n_head




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						PreTrainedTokenizer						, __lowerCAmelCase					:						int = -1						, __lowerCAmelCase					:						int = -1						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						Optional[TensorType] = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			super(__lowerCAmelCase						, self		).generate_dummy_inputs(
             __lowerCAmelCase						, batch_size=__lowerCAmelCase						, seq_length=__lowerCAmelCase						, is_pair=__lowerCAmelCase						, framework=__lowerCAmelCase		)

         # We need to order the input in the way they appears in the forward()
         _lowerCamelCase						:    int         =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

         # Need to add the past_keys
         if self.use_past:
                if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
                else:
                       import torch

                       _lowerCamelCase						:    int         =			common_inputs['''input_ids'''].shape
                       # Not using the same length for past_key_values
                       _lowerCamelCase						:    Dict         =			seqlen + 2
                       _lowerCamelCase						:    Tuple         =			(
                           batch,
                           self.num_attention_heads,
                           past_key_values_length,
                           self._config.hidden_size // self.num_attention_heads,
                       )
                       _lowerCamelCase						:    Union[str, Any]         =			[
                           (torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		)) for _ in range(self.num_layers		)
                       ]

         _lowerCamelCase						:    Any         =			common_inputs['''attention_mask''']
         if self.use_past:
                _lowerCamelCase						:    Tuple         =			ordered_inputs['''attention_mask'''].dtype
                _lowerCamelCase						:    Union[str, Any]         =			torch.cat(
                    [ordered_inputs['''attention_mask'''], torch.ones(__lowerCAmelCase						, __lowerCAmelCase						, dtype=__lowerCAmelCase		)]						, dim=1		)

         return ordered_inputs




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         return 1_3







"""simple docstring"""






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						BlenderbotSmallTokenizer
  snake_case__    :       List[str]        =						False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         super().setUp()

         _lowerCamelCase						:    str         =			['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
         _lowerCamelCase						:    Any         =			dict(zip(__lowerCAmelCase						, range(len(__lowerCAmelCase		)		)		)		)

         _lowerCamelCase						:    Any         =			['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
         _lowerCamelCase						:    List[str]         =			{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

         _lowerCamelCase						:    Dict         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:    List[str]         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''merges_file''']		)
         with open(self.vocab_file						, '''w'''						, encoding='''utf-8'''		) as fp:
                fp.write(json.dumps(__lowerCAmelCase		) + '''\n'''		)
         with open(self.merges_file						, '''w'''						, encoding='''utf-8'''		) as fp:
                fp.write('''\n'''.join(__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Tuple         =			'''adapt act apte'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			BlenderbotSmallTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map		)
         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Optional[Any]         =			['''adapt''', '''act''', '''ap@@''', '''te''']
         _lowerCamelCase						:    Optional[Any]         =			tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         _lowerCamelCase						:    Dict         =			[0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         assert tok('''sam'''		).input_ids == [1_3_8_4]
         _lowerCamelCase						:    List[str]         =			'''I am a small frog.'''
         _lowerCamelCase						:    str         =			tok([src_text]						, padding=__lowerCAmelCase						, truncation=__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Any         =			tok.batch_decode(__lowerCAmelCase						, skip_special_tokens=__lowerCAmelCase						, clean_up_tokenization_spaces=__lowerCAmelCase		)[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         _lowerCamelCase						:    Optional[Any]         =			'''I am a small frog .'''
         _lowerCamelCase						:    str         =			'''.'''
         _lowerCamelCase						:    str         =			tok(__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Dict         =			tok(__lowerCAmelCase		)['''input_ids''']

         assert encoded[-1] == encoded_dot[0]


