



'''simple docstring'''

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 	__magic_name__						(    unittest.TestCase   ):







							@property
							def 							__lowercase			(							self					:       str		):
									torch.manual_seed(0		)
									_a     :      str        =							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 							__lowercase			(							self					:       Any		):
									_a     :      Any        =							self.dummy_uncond_unet
									_a     :      Tuple        =							PNDMScheduler()

									_a     :      Tuple        =							PNDMPipeline(unet=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									pndm.to(_UpperCAmelCase		)
									pndm.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a     :      Tuple        =							torch.manual_seed(0		)
									_a     :      Any        =							pndm(generator=_UpperCAmelCase	,num_inference_steps=20	,output_type='numpy'		).images

									_a     :      Optional[int]        =							torch.manual_seed(0		)
									_a     :      Dict        =							pndm(generator=_UpperCAmelCase	,num_inference_steps=20	,output_type='numpy'	,return_dict=_UpperCAmelCase		)[0]

									_a     :      Union[str, Any]        =							image[0, -3:, -3:, -1]
									_a     :      Tuple        =							image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_a     :      Any        =							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 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[str]        =							'google/ddpm-cifar10-32'

									_a     :      int        =							UNetaDModel.from_pretrained(_UpperCAmelCase		)
									_a     :      List[str]        =							PNDMScheduler()

									_a     :      Union[str, Any]        =							PNDMPipeline(unet=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									pndm.to(_UpperCAmelCase		)
									pndm.set_progress_bar_config(disable=_UpperCAmelCase		)
									_a     :      List[Any]        =							torch.manual_seed(0		)
									_a     :      Optional[int]        =							pndm(generator=_UpperCAmelCase	,output_type='numpy'		).images

									_a     :      int        =							image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_a     :      int        =							np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]		)

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









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_							: Optional[Any]						 =	{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: List[str]						 =	["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Dict						 =	["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Optional[int]						 =	[
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   A_							: Any						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



