

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						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 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						int					=					KarrasVeScheduler()

       __lowerCAmelCase					:						Any					=					KarrasVePipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pipe(num_inference_steps=2      ,							generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						str					=					pipe(num_inference_steps=2      ,							generator=A_      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						Optional[int]					=					np.array([0.0, 1.0, 0.0, 0.0, 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   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					'''google/ncsnpp-celebahq-256'''
       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					KarrasVeScheduler()

       __lowerCAmelCase					:						Optional[int]					=					KarrasVePipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Tuple					=					pipe(num_inference_steps=20      ,							generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Union[str, Any]					=					image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       __lowerCAmelCase					:						str					=					np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]					)

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





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


