





'''simple docstring'''


from typing import Mapping

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "t5"
 __lowerCamelCase    :				Optional[Any]								=       ["past_key_values"]
 __lowerCamelCase    :				Dict								=       {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




 def __init__(						self      ,  _lowerCAmelCase=32128      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=64      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=6      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-6      ,  _lowerCAmelCase=1.0      ,  _lowerCAmelCase="relu"      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  **_lowerCAmelCase      ,  )     ->				Optional[int]:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   d_kv
     _lowerCAmelCase						      =   d_ff
     _lowerCAmelCase						      =   num_layers
     _lowerCAmelCase						      =   (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   relative_attention_num_buckets
     _lowerCAmelCase						      =   relative_attention_max_distance
     _lowerCAmelCase						      =   dropout_rate
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_factor
     _lowerCAmelCase						      =   feed_forward_proj
     _lowerCAmelCase						      =   use_cache

     _lowerCAmelCase						      =   self.feed_forward_proj.split("-"		)
     _lowerCAmelCase						      =   act_info[-1]
     _lowerCAmelCase						      =   act_info[0] == "gated"

     if len(_lowerCAmelCase		) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase		) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "'gated-gelu' or 'relu'"		)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         _lowerCAmelCase						      =   "gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  is_encoder_decoder=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase						      =   {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase						      =   "past_encoder_sequence + sequence"
         _lowerCAmelCase						      =   {0: "batch"}
         _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase      ,  direction="inputs"		)

     return common_inputs





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







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      (PNDMScheduler,)
     _A      =      (('num_inference_steps', 50),)







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**__a     )
           return config







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

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

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

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







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__a     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

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

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

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







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(**__a     )
           lowerCamelCase             =		scheduler_class(**__a     )

           lowerCamelCase             =		10
           lowerCamelCase             =		self.dummy_model()
           lowerCamelCase             =		self.dummy_sample_deter
           scheduler.set_timesteps(__a     )

           for i, t in enumerate(scheduler.prk_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a     ).prev_sample

           return sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )

           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

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

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

                 lowerCamelCase             =		scheduler.step_prk(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_prk(__a					,      1					,      __a					,      **__a     ).prev_sample

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

                 lowerCamelCase             =		scheduler.step_plms(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_plms(__a					,      1					,      __a					,      **__a     ).prev_sample

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







     def     _a   (self     ):






           '''simple docstring'''



           for timesteps in [1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__a     )

           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(steps_offset=1     )
           lowerCamelCase             =		scheduler_class(**__a     )
           scheduler.set_timesteps(10     )
           assert torch.equal(
               scheduler.timesteps					,      torch.LongTensor(
                   [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]     )					,      )







     def     _a   (self     ):






           '''simple docstring'''



           for beta_start, beta_end in zip([0.0001, 0.001]					,      [0.002, 0.02]     ):
                 self.check_over_configs(beta_start=__a					,      beta_end=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t in [1, 5, 10]:
                 self.check_over_forward(time_step=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 1_00]     ):
                 self.check_over_forward(num_inference_steps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		27

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.set_timesteps(__a     )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
                       lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           with self.assertRaises(__a     ):
                 lowerCamelCase             =		self.scheduler_classes[0]
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop()
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 198.1318     ) < 1E-2
           assert abs(result_mean.item() - 0.2580     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(prediction_type="v_prediction"     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 67.3986     ) < 1E-2
           assert abs(result_mean.item() - 0.0878     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 230.0399     ) < 1E-2
           assert abs(result_mean.item() - 0.2995     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 186.9482     ) < 1E-2
           assert abs(result_mean.item() - 0.2434     ) < 1E-3