



'''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


lowercase_									=    logging.get_logger(__name__)


lowercase_									=    {
    "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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			int		=			'codegen'
 __lowerCamelCase       :			Dict		=			{
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self					,  A=50_400					,  A=2_048					,  A=2_048					,  A=4_096					,  A=28					,  A=16					,  A=64					,  A=None					,  A="gelu_new"					,  A=0.0					,  A=0.0					,  A=0.0					,  A=1E-5					,  A=0.02					,  A=True					,  A=50_256					,  A=50_256					,  A=False					,  **A					,  )       ->	Optional[Any]:


      """simple docstring"""



      _a           =    vocab_size
      _a           =    n_ctx
      _a           =    n_positions
      _a           =    n_embd
      _a           =    n_layer
      _a           =    n_head
      _a           =    n_inner
      _a           =    rotary_dim
      _a           =    activation_function
      _a           =    resid_pdrop
      _a           =    embd_pdrop
      _a           =    attn_pdrop
      _a           =    layer_norm_epsilon
      _a           =    initializer_range
      _a           =    use_cache

      _a           =    bos_token_id
      _a           =    eos_token_id

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






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  A = "default"					,  A = None					,  A = False					,  )       ->	Optional[Any]:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a           =    OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
      if self.use_past:
           self.fill_with_past_key_values_(A					,  direction='''inputs'''			)
           _a           =    {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a           =    {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self._config.n_head



 def    a__    (self					,  A					,  A = -1					,  A = -1					,  A = False					,  A = None					,  )       ->	Mapping[str, Any]:


      """simple docstring"""



      _a           =    super(A					,  self			).generate_dummy_inputs(
          A					,  batch_size=A					,  seq_length=A					,  is_pair=A					,  framework=A			)

      # We need to order the input in the way they appears in the forward()
      _a           =    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

                _a    ,       _a           =    common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a           =    seqlen + 2
                _a           =    (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a           =    [
                    (torch.zeros(A			), torch.zeros(A			)) for _ in range(self.num_layers			)
                ]

      _a           =    common_inputs['''attention_mask''']
      if self.use_past:
           _a           =    ordered_inputs['''attention_mask'''].dtype
           _a           =    torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A					,  A					,  dtype=A			)]					,  dim=1			)

      return ordered_inputs



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return 13





'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,  [0.002, 0.02, 0.2, 2] ):
																		self.check_over_configs(beta_start=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.check_over_configs(thresholding=_lowercase )
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=_lowercase       ,  prediction_type=_lowercase       ,  sample_max_value=_lowercase       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 258.9606 ) < 1e-2
												assert abs(result_mean.item() - 0.3372 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 202.0296 ) < 1e-2
												assert abs(result_mean.item() - 0.2631 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

												for i, timestep in enumerate(_lowercase ):
																		if i == len(_lowercase ) - 1:
																								_lowerCAmelCase    		=					-1
																		else:
																								_lowerCAmelCase    		=					timesteps[i + 1]

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 51, 0]

												with self.assertRaises(_lowercase       ,  msg="""`custom_timesteps` must be in descending order.""" ):
																		scheduler.set_timesteps(timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

												with self.assertRaises(_lowercase       ,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase       ,  timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _lowercase       ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,  ):
																		scheduler.set_timesteps(timesteps=_lowercase )
