




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


SCREAMING_SNAKE_CASE__       :      Any  =   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      """codegen"""
    lowerCamelCase_      :	Optional[int]	     =      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(    self     ,  UpperCamelCase__=5_0400     ,  UpperCamelCase__=2048     ,  UpperCamelCase__=2048     ,  UpperCamelCase__=4096     ,  UpperCamelCase__=28     ,  UpperCamelCase__=16     ,  UpperCamelCase__=64     ,  UpperCamelCase__=None     ,  UpperCamelCase__="gelu_new"     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=1e-5     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=True     ,  UpperCamelCase__=5_0256     ,  UpperCamelCase__=5_0256     ,  UpperCamelCase__=False     ,  **UpperCamelCase__     ,  )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					vocab_size
           lowerCamelCase   :      Optional[Any]			 =					n_ctx
           lowerCamelCase   :      Optional[int]			 =					n_positions
           lowerCamelCase   :      int			 =					n_embd
           lowerCamelCase   :      Optional[Any]			 =					n_layer
           lowerCamelCase   :      List[Any]			 =					n_head
           lowerCamelCase   :      Optional[int]			 =					n_inner
           lowerCamelCase   :      Optional[int]			 =					rotary_dim
           lowerCamelCase   :      int			 =					activation_function
           lowerCamelCase   :      List[str]			 =					resid_pdrop
           lowerCamelCase   :      Optional[int]			 =					embd_pdrop
           lowerCamelCase   :      Tuple			 =					attn_pdrop
           lowerCamelCase   :      int			 =					layer_norm_epsilon
           lowerCamelCase   :      Optional[Any]			 =					initializer_range
           lowerCamelCase   :      str			 =					use_cache

           lowerCamelCase   :      List[Any]			 =					bos_token_id
           lowerCamelCase   :      Tuple			 =					eos_token_id

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




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = "default"     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  )     ->      List[Any]:
           super().__init__(UpperCamelCase__     ,  task=UpperCamelCase__     ,  patching_specs=UpperCamelCase__     ,  use_past=UpperCamelCase__     )
           if not getattr(self._config     ,  "pad_token_id"     ,  UpperCamelCase__     ):
                  # TODO: how to do that better?
                  lowerCamelCase   :      Union[str, Any]			 =					0







    @property
    def     _lowercase     (    self     )     ->      Mapping[str, Mapping[int, str]]:
           lowerCamelCase   :      List[Any]			 =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
           if self.use_past:
                  self.fill_with_past_key_values_(UpperCamelCase__     ,  direction="inputs"     )
                  lowerCamelCase   :      Optional[Any]			 =					{0: "batch", 1: "past_sequence + sequence"}
           else:
                  lowerCamelCase   :      Dict			 =					{0: "batch", 1: "sequence"}

           return common_inputs







    @property
    def     _lowercase     (    self     )     ->      int:
           return self._config.n_layer







    @property
    def     _lowercase     (    self     )     ->      int:
           return self._config.n_head







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  )     ->      Mapping[str, Any]:
           lowerCamelCase   :      int			 =					super(UpperCamelCase__     ,  self     ).generate_dummy_inputs(
               UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  seq_length=UpperCamelCase__     ,  is_pair=UpperCamelCase__     ,  framework=UpperCamelCase__     )

           # 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 ,      lowerCamelCase   :      Optional[int]			 =					common_inputs["input_ids"].shape
                         # Not using the same length for past_key_values
                         lowerCamelCase   :      List[Any]			 =					seqlen + 2
                         lowerCamelCase   :      Any			 =					(
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowerCamelCase   :      Union[str, Any]			 =					[
                             (torch.zeros(UpperCamelCase__     ), torch.zeros(UpperCamelCase__     )) for _ in range(self.num_layers     )
                         ]

           lowerCamelCase   :      str			 =					common_inputs["attention_mask"]
           if self.use_past:
                  lowerCamelCase   :      Optional[Any]			 =					ordered_inputs["attention_mask"].dtype
                  lowerCamelCase   :      Union[str, Any]			 =					torch.cat(
                      [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__     ,  UpperCamelCase__     ,  dtype=UpperCamelCase__     )]     ,  dim=1     )

           return ordered_inputs







    @property
    def     _lowercase     (    self     )     ->      int:
           return 13










'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      List[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          new_scheduler.set_timesteps(lowerCamelCase		)
          # copy over dummy past residuals
          lowerCAmelCase_			:      str			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      int			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      Optional[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(lowerCamelCase		)

          # copy over dummy past residual (must be after setting timesteps)
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Optional[Any]		)   ->  str:
      lowerCAmelCase_			:      Optional[int]			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      List[Any]			=   self.get_scheduler_config(**lowerCamelCase		)
      lowerCAmelCase_			:      str			=   scheduler_class(**lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      Tuple			=   0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(lowerCamelCase		)
        elif num_inference_steps is not None and not hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape		)
        self.assertEqual(output_a.shape    ,						output_a.shape		)

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape		)
        self.assertEqual(output_a.shape    ,						output_a.shape		)

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[int]:
      for t, num_inference_steps in zip([1, 5, 10]    ,						[10, 50, 1_00]		):
        self.check_over_forward(num_inference_steps=lowerCamelCase    ,						time_step=lowerCamelCase		)





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10



