





'''simple docstring'''






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin




@require_jieba
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[Any]			=  CpmAntTokenizer
      _lowercase:     List[Any]			=  False




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[int]:
            super().setUp()

            _lowerCAmelCase					=				[
                """<d>""",
                """</d>""",
                """<s>""",
                """</s>""",
                """</_>""",
                """<unk>""",
                """<pad>""",
                """</n>""",
                """我""",
                """是""",
                """C""",
                """P""",
                """M""",
                """A""",
                """n""",
                """t""",
            ]
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )




      @tooslow
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Any:
            _lowerCAmelCase					=				CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""    )
            _lowerCAmelCase					=				"""今天天气真好！"""
            _lowerCAmelCase					=				["""今天""", """天气""", """真""", """好""", """！"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				"""今天天气真好！"""
            _lowerCAmelCase					=				[tokenizer.bos_token] + tokens

            _lowerCAmelCase					=				[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )

            _lowerCAmelCase					=				tokenizer.decode(__snake_case    )
            self.assertEqual(__snake_case     ,     __snake_case    )













'''simple docstring'''






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

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


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






