

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class  __lowerCAmelCase	(      _UpperCamelCase					):
			lowerCamelCase_					:					Any	      =				'gpt_neo'
			lowerCamelCase_					:					List[str]	      =				['past_key_values']
			lowerCamelCase_					:					str	      =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}






			def __init__(self ,							__magic_name__=5_0257 ,							__magic_name__=2048 ,							__magic_name__=2048 ,							__magic_name__=24 ,							__magic_name__=[[["global", "local"], 12]] ,							__magic_name__=16 ,							__magic_name__=None ,							__magic_name__=256 ,							__magic_name__="gelu_new" ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=0.1 ,							__magic_name__=1e-5 ,							__magic_name__=0.02 ,							__magic_name__=True ,							__magic_name__=5_0256 ,							__magic_name__=5_0256 ,							**__magic_name__ ,							)					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  vocab_size
							snake_case_						:   Any           =  max_position_embeddings
							snake_case_						:   Union[str, Any]           =  hidden_size
							snake_case_						:   Tuple           =  num_layers
							snake_case_						:   Optional[Any]           =  num_heads
							snake_case_						:   List[str]           =  intermediate_size
							snake_case_						:   str           =  window_size
							snake_case_						:   str           =  activation_function
							snake_case_						:   List[Any]           =  resid_dropout
							snake_case_						:   Optional[Any]           =  embed_dropout
							snake_case_						:   Tuple           =  attention_dropout
							snake_case_						:   List[str]           =  classifier_dropout
							snake_case_						:   List[str]           =  layer_norm_epsilon
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Any           =  use_cache

							snake_case_						:   Tuple           =  bos_token_id
							snake_case_						:   List[str]           =  eos_token_id

							snake_case_						:   List[str]           =  attention_types
							snake_case_						:   Optional[Any]           =  self.expand_attention_types_params(_SCREAMING_SNAKE_CASE   )

							if len(self.attention_layers   ) != self.num_layers:
											raise ValueError(
											    '''Configuration for convolutional module is incorrect. '''
											    '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
											    F'''but is `len(config.attention_layers) = {len(self.attention_layers   )}`, '''
											    F'''`config.num_layers = {self.num_layers}`. '''
											    '''`config.attention_layers` is prepared using `config.attention_types`. '''
											    '''Please verify the value of `config.attention_types` argument.'''   )

							super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE ,							eos_token_id=_SCREAMING_SNAKE_CASE ,							**_SCREAMING_SNAKE_CASE   )






			@staticmethod
			def       lowerCamelCase  (__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  []
							for item in attention_types:
											for _ in range(item[1]   ):
															attentions.extend(item[0]   )
							return attentions






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				import torch

				snake_case_						:   Union[str, Any]           =  input.size()
				snake_case_						:   List[Any]           =  len(_UpperCamelCase			)
				snake_case_						:   Any           =  shape[dimension]

				snake_case_						:   str           =  torch.arange(0			,    _UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  torch.div(sizedim - size			,    _UpperCamelCase			,    rounding_mode='''floor'''			) + 1
				snake_case_						:   Optional[int]           =  torch.arange(_UpperCamelCase			) + low_indices[:min_length][:, None]

				snake_case_						:   List[str]           =  [slice(_UpperCamelCase			)] * rank
				snake_case_						:   Optional[int]           =  indices
				snake_case_						:   Union[str, Any]           =  input[s]

				snake_case_						:   int           =  list(range(0			,    rank + 1			)			)
				perm.append(perm.pop(dimension + 1			)			)

				return sliced.permute(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				import torch

				snake_case_						:   str           =  torch.arange(1			,    _UpperCamelCase			)
				snake_case_						:   Dict           =  torch.remainder(_UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   Dict           =  remainders == 0
				snake_case_						:   List[Any]           =  candidates[divisor_indices]
				snake_case_						:   int           =  torch.max(_UpperCamelCase			)
				return largest_divisor, torch.div(_UpperCamelCase			,    _UpperCamelCase			,    rounding_mode='''floor'''			)





class  __lowerCAmelCase	(      _UpperCamelCase					):






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
							if self.use_past:
											self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE ,							direction='''inputs'''   )
											snake_case_						:   int           =  {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
											snake_case_						:   Union[str, Any]           =  {0: '''batch''', 1: '''sequence'''}

							return common_inputs






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return self._config.num_heads






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


							'''simple docstring'''



							snake_case_						:   Any           =  super(_SCREAMING_SNAKE_CASE ,							self   ).generate_dummy_inputs(
							    _SCREAMING_SNAKE_CASE ,							batch_size=_SCREAMING_SNAKE_CASE ,							seq_length=_SCREAMING_SNAKE_CASE ,							is_pair=_SCREAMING_SNAKE_CASE ,							framework=_SCREAMING_SNAKE_CASE   )

							# We need to order the input in the way they appears in the forward()
							snake_case_						:   List[str]           =  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

															snake_case_      ,							snake_case_						:   Dict           =  common_inputs['''input_ids'''].shape
															# Not using the same length for past_key_values
															snake_case_						:   int           =  seqlen + 2
															snake_case_						:   Tuple           =  (
															    batch,
															    self.num_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // self.num_attention_heads,
															)
															snake_case_						:   Any           =  [
															    (torch.zeros(_SCREAMING_SNAKE_CASE   ), torch.zeros(_SCREAMING_SNAKE_CASE   )) for _ in range(self.num_layers   )
															]

							snake_case_						:   str           =  common_inputs['''attention_mask''']
							if self.use_past:
											snake_case_						:   Optional[Any]           =  ordered_inputs['''attention_mask'''].dtype
											snake_case_						:   int           =  torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							dtype=_SCREAMING_SNAKE_CASE   )] ,							dim=1   )

							return ordered_inputs






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return 13







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  UNetaDModel(
							    block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,							)
							return model






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
