
import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          CpmAntConfig,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntTokenizer,
      )






@require_torch
class     a_       :



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=2				,       _lowerCamelCase=8				,       _lowerCamelCase=True				,       _lowerCamelCase=False				,       _lowerCamelCase=False				,       _lowerCamelCase=False				,       _lowerCamelCase=False				,       _lowerCamelCase=99				,       _lowerCamelCase=32				,       _lowerCamelCase=3				,       _lowerCamelCase=4				,       _lowerCamelCase=37				,       _lowerCamelCase=32				,       _lowerCamelCase=128				,       _lowerCamelCase=8				,       _lowerCamelCase=8				,       _lowerCamelCase=8				,       _lowerCamelCase=1.0				,       _lowerCamelCase=True				,       )					->int:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = parent
           SCREAMING_SNAKE_CASE				:				str				      = batch_size
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = seq_length
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = is_training
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = use_token_type_ids
           SCREAMING_SNAKE_CASE				:				List[str]				      = use_input_mask
           SCREAMING_SNAKE_CASE				:				Any				      = use_labels
           SCREAMING_SNAKE_CASE				:				Tuple				      = use_mc_token_ids
           SCREAMING_SNAKE_CASE				:				Dict				      = vocab_size
           SCREAMING_SNAKE_CASE				:				List[str]				      = hidden_size
           SCREAMING_SNAKE_CASE				:				Any				      = num_hidden_layers
           SCREAMING_SNAKE_CASE				:				int				      = num_attention_heads
           SCREAMING_SNAKE_CASE				:				List[str]				      = intermediate_size
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = num_buckets
           SCREAMING_SNAKE_CASE				:				List[str]				      = max_distance
           SCREAMING_SNAKE_CASE				:				Any				      = prompt_length
           SCREAMING_SNAKE_CASE				:				Dict				      = prompt_types
           SCREAMING_SNAKE_CASE				:				str				      = segment_types
           SCREAMING_SNAKE_CASE				:				List[str]				      = init_std
           SCREAMING_SNAKE_CASE				:				Tuple				      = return_dict






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				int				      = {}
           SCREAMING_SNAKE_CASE				:				List[Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   ).type(torch.intaa   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = False

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_config()

           return (config, input_ids)






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           return CpmAntConfig(
               vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       dim_ff=self.intermediate_size				,       position_bias_num_buckets=self.num_buckets				,       position_bias_max_distance=self.max_distance				,       prompt_types=self.prompt_types				,       prompt_length=self.prompt_length				,       segment_types=self.segment_types				,       use_cache=snake_case_				,       init_std=self.init_std				,       return_dict=self.return_dict				,       )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       *_lowerCamelCase   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = CpmAntModel(config=snake_case_   )
           model.to(snake_case_   )
           model.eval()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = model(**snake_case_   ).last_hidden_state

           self.parent.assertEqual(hidden_states.shape				,       (self.batch_size, self.seq_length, config.hidden_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       *_lowerCamelCase   )					->Any:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = CpmAntForCausalLM(snake_case_   )
           model.to(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = input_ids["input_ids"].to(snake_case_   )
           model.eval()

           SCREAMING_SNAKE_CASE				:				List[str]				      = model(**snake_case_   )
           self.parent.assertEqual(
               model_output.logits.shape				,       (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)				,       )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Any				      = self.prepare_config_and_inputs()
           return config, inputs_dict






@require_torch
class     a_       (			_snake_case				,    _snake_case				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[str]						=  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  (
           {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
       )

       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  False
       __SCREAMING_SNAKE_CASE       :					int						=  False
       __SCREAMING_SNAKE_CASE       :					List[str]						=  False
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  False
       __SCREAMING_SNAKE_CASE       :					Optional[Any]						=  False






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				str				      = CpmAntModelTester(self   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = ConfigTester(self				,       config_class=snake_case_   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           self.config_tester.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           unittest.skip('''CPMAnt doesn\'t support input_embeds.'''   )(self.test_inputs_embeds   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           unittest.skip(
               '''CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'''   )(self.test_retain_grad_hidden_states_attentions   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_cpmant_model(snake_case_				,       snake_case_   )






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(snake_case_				,       snake_case_   )






@require_torch
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       @tooslow
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = "今天天气真好！"
           SCREAMING_SNAKE_CASE				:				str				      = "openbmb/cpm-ant-10b"
           SCREAMING_SNAKE_CASE				:				str				      = CpmAntModel.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = CpmAntTokenizer.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Dict				      = tokenizer(snake_case_				,       return_tensors='''pt'''   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(**snake_case_   ).last_hidden_state

           SCREAMING_SNAKE_CASE				:				Tuple				      = torch.tensor(
               [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]				,       )
           self.assertTrue(torch.allclose(hidden_states[:, :3, :3]				,       snake_case_				,       atol=1e-2   )   )






@require_torch
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       @tooslow
       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Any				      = "今天天气真好！"
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = "openbmb/cpm-ant-10b"
           SCREAMING_SNAKE_CASE				:				int				      = CpmAntForCausalLM.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = CpmAntTokenizer.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = tokenizer(snake_case_				,       return_tensors='''pt'''   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(**snake_case_   ).logits

           SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor(
               [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]				,       )
           self.assertTrue(torch.allclose(hidden_states[:, :3, :3]				,       snake_case_				,       atol=1e-2   )   )






       @tooslow
       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = "openbmb/cpm-ant-10b"
           SCREAMING_SNAKE_CASE				:				str				      = CpmAntForCausalLM.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = CpmAntTokenizer.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = "今天天气不错，"
           SCREAMING_SNAKE_CASE				:				Any				      = "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
           SCREAMING_SNAKE_CASE				:				List[str]				      = tokenizer(snake_case_				,       return_tensors='''pt'''   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = model.generate(**snake_case_   )
           SCREAMING_SNAKE_CASE				:				str				      = tokenizer.batch_decode(snake_case_   )
           self.assertEqual(snake_case_				,       snake_case_   )






       @tooslow
       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = "openbmb/cpm-ant-10b"
           SCREAMING_SNAKE_CASE				:				str				      = CpmAntForCausalLM.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				str				      = CpmAntTokenizer.from_pretrained(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ["今天天气不错，", "新年快乐，万事如意！"]
           SCREAMING_SNAKE_CASE				:				Dict				      = [
               "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
               "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
           ]
           SCREAMING_SNAKE_CASE				:				List[str]				      = tokenizer(snake_case_				,       return_tensors='''pt'''				,       padding=snake_case_   )
           SCREAMING_SNAKE_CASE				:				Any				      = model.generate(**snake_case_   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = tokenizer.batch_decode(snake_case_   )
           self.assertEqual(snake_case_				,       snake_case_   )


"""simple docstring"""
import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_a   :      Optional[int]       =				logging.get_logger(__name__)

_a   :      Tuple       =				OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

_a   :      Any       =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def 					a__ ( a :					str		):

  """simple docstring"""







  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _snake_case  :    Dict            = model_type_to_module_name(a		)

      _snake_case  :    Optional[int]            = importlib.import_module(f'.{module_name}'							,  "transformers.models"		)
      try:
        return getattr(a							,  a		)
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(a							,  "__name__"							,  a		) == class_name:
      return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _snake_case  :    int            = importlib.import_module("transformers"		)
  if hasattr(a							,  a		):
    return getattr(a							,  a		)

  return None






class   _UpperCAmelCase					:
    def __init__(     self  ):
      raise EnvironmentError(
          "AutoProcessor is designed to be instantiated "
          "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."  )
    @classmethod
    @replace_list_option_in_docstrings(snake_case_  )
    def        lowerCamelCase__						(     cls    ,     snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = kwargs.pop("config"    ,     snake_case_  )
      _snake_case  :    Dict            = kwargs.pop("trust_remote_code"    ,     snake_case_  )
      _snake_case  :    Tuple            = True

      _snake_case  :    List[str]            = None
      _snake_case  :    Optional[Any]            = None

      # First, let's see if we have a preprocessor config.
      # Filter the kwargs for `get_file_from_repo`.
      _snake_case  :    Optional[int]            = {
          key: kwargs[key] for key in inspect.signature(snake_case_  ).parameters.keys() if key in kwargs
      }
      # Let's start by checking whether the processor class is saved in an image processor
      _snake_case  :    Dict            = get_file_from_repo(
          snake_case_    ,     snake_case_    ,     **snake_case_  )
      if preprocessor_config_file is not None:
        _snake_case      ,       _snake_case  :    List[str]            = ImageProcessingMixin.get_image_processor_dict(snake_case_    ,     **snake_case_  )
        _snake_case  :    Union[str, Any]            = config_dict.get("processor_class"    ,     snake_case_  )
        if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
          _snake_case  :    Optional[int]            = config_dict["auto_map"]["AutoProcessor"]

        # If not found, let's check whether the processor class is saved in a feature extractor config
      if preprocessor_config_file is not None and processor_class is None:
        _snake_case      ,       _snake_case  :    Any            = FeatureExtractionMixin.get_feature_extractor_dict(snake_case_    ,     **snake_case_  )
        _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
        if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
          _snake_case  :    int            = config_dict["auto_map"]["AutoProcessor"]

      if processor_class is None:
        # Next, let's check whether the processor class is saved in a tokenizer
        _snake_case  :    Union[str, Any]            = get_file_from_repo(
            snake_case_    ,     snake_case_    ,     **snake_case_  )
        if tokenizer_config_file is not None:
          with open(snake_case_    ,     encoding="utf-8"  ) as reader:
            _snake_case  :    Tuple            = json.load(snake_case_  )

          _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
          if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
            _snake_case  :    Tuple            = config_dict["auto_map"]["AutoProcessor"]

      if processor_class is None:
        # Otherwise, load config, if it can be loaded.
        if not isinstance(snake_case_    ,     snake_case_  ):
          _snake_case  :    Union[str, Any]            = AutoConfig.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )

        # And check if the config contains the processor class.
        _snake_case  :    List[Any]            = getattr(snake_case_    ,     "processor_class"    ,     snake_case_  )
        if hasattr(snake_case_    ,     "auto_map"  ) and "AutoProcessor" in config.auto_map:
          _snake_case  :    str            = config.auto_map["AutoProcessor"]

      if processor_class is not None:
        _snake_case  :    Optional[int]            = processor_class_from_name(snake_case_  )

      _snake_case  :    Optional[int]            = processor_auto_map is not None
      _snake_case  :    Any            = processor_class is not None or type(snake_case_  ) in PROCESSOR_MAPPING
      _snake_case  :    Union[str, Any]            = resolve_trust_remote_code(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

      if has_remote_code and trust_remote_code:
        _snake_case  :    Dict            = get_class_from_dynamic_module(
            snake_case_    ,     snake_case_    ,     **snake_case_  )
        _snake_case  :    Union[str, Any]            = kwargs.pop("code_revision"    ,     snake_case_  )
        if os.path.isdir(snake_case_  ):
          processor_class.register_for_auto_class()
        return processor_class.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      elif processor_class is not None:
        return processor_class.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      # Last try: we use the PROCESSOR_MAPPING.
      elif type(snake_case_  ) in PROCESSOR_MAPPING:
        return PROCESSOR_MAPPING[type(snake_case_  )].from_pretrained(snake_case_    ,     **snake_case_  )

      # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
      # tokenizer.
      try:
        return AutoTokenizer.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      except Exception:
        try:
          return AutoImageProcessor.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
        except Exception:
          pass

        try:
          return AutoFeatureExtractor.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
        except Exception:
          pass

      raise ValueError(
          F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
          "tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"
          "the files of at least one of those processing classes."  )


    @staticmethod
    def        lowerCamelCase__						(     snake_case_    ,     snake_case_  ):
      PROCESSOR_MAPPING.register(snake_case_    ,     snake_case_  )

