


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       StableUnCLIPImgaImgPipeline
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 SCREAMING_SNAKE_CASE__      =       frozenset(
     []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 SCREAMING_SNAKE_CASE__      =       frozenset([]							)




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       32
       a						:Tuple 							=       embedder_hidden_size

       # image encoding components

       a						:Optional[Any] 							=       CLIPImageProcessor(crop_size=32					,  size=32    )

       torch.manual_seed(0    )
       a						:Optional[int] 							=       CLIPVisionModelWithProjection(
           CLIPVisionConfig(
               hidden_size=_lowerCamelCase					,  projection_dim=_lowerCamelCase					,  num_hidden_layers=5					,  num_attention_heads=4					,  image_size=32					,  intermediate_size=37					,  patch_size=1					,  )    )

       # regular denoising components

       torch.manual_seed(0    )
       a						:int 							=       StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase    )
       a						:List[str] 							=       DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''    )

       torch.manual_seed(0    )
       a						:Tuple 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

       torch.manual_seed(0    )
       a						:str 							=       CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0					,  eos_token_id=2					,  hidden_size=_lowerCamelCase					,  projection_dim=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  )    )

       torch.manual_seed(0    )
       a						:int 							=       UNetaDConditionModel(
           sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')					,  up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')					,  block_out_channels=(32, 64)					,  attention_head_dim=(2, 4)					,  class_embed_type='''projection'''					,  projection_class_embeddings_input_dim=embedder_projection_dim * 2					,  cross_attention_dim=_lowerCamelCase					,  layers_per_block=1					,  upcast_attention=_lowerCamelCase					,  use_linear_projection=_lowerCamelCase					,  )

       torch.manual_seed(0    )
       a						:Tuple 							=       DDIMScheduler(
           beta_schedule='''scaled_linear'''					,  beta_start=0.0_0085					,  beta_end=0.012					,  prediction_type='''v_prediction'''					,  set_alpha_to_one=_lowerCamelCase					,  steps_offset=1					,  )

       torch.manual_seed(0    )
       a						:Union[str, Any] 							=       AutoencoderKL()

       a						:Optional[Any] 							=       {
           # image encoding components
           '''feature_extractor''': feature_extractor,
           '''image_encoder''': image_encoder.eval(),
           # image noising components
           '''image_normalizer''': image_normalizer.eval(),
           '''image_noising_scheduler''': image_noising_scheduler,
           # regular denoising components
           '''tokenizer''': tokenizer,
           '''text_encoder''': text_encoder.eval(),
           '''unet''': unet.eval(),
           '''scheduler''': scheduler,
           '''vae''': vae.eval(),
       }

       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0					,  _lowerCamelCase=True    ):
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Optional[int] 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:List[Any] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )

       a						:List[str] 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )

       if pil_image:
             a						:Any 							=       input_image * 0.5 + 0.5
             a						:Optional[int] 							=       input_image.clamp(0					,  1    )
             a						:List[str] 							=       input_image.cpu().permute(0					,  2					,  3					,  1    ).float().numpy()
             a						:List[str] 							=       DiffusionPipeline.numpy_to_pil(_lowerCamelCase    )[0]

       return {
           "prompt": "An anime racoon running a marathon",
           "image": input_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "np",
       }




 @skip_mps
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:Any 							=       self.get_dummy_components()
       a						:Tuple 							=       StableUnCLIPImgaImgPipeline(**_lowerCamelCase    )
       a						:List[Any] 							=       sd_pipe.to(_lowerCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:int 							=       self.get_dummy_inputs(_lowerCamelCase    )
       inputs.update({'''image_embeds''': None}    )
       a						:Dict 							=       sd_pipe(**_lowerCamelCase    ).images
       a						:Union[str, Any] 							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a						:List[str] 							=       np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       torch_device in ['''cpu''', '''mps''']

       self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase    )




 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_lowerCamelCase    )







@slow
@require_torch_gpu
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''    )

       a						:Any 							=       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''    )

       a						:Optional[int] 							=       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-l-img2img'''					,  torch_dtype=torch.floataa    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       a						:Tuple 							=       torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a						:List[str] 							=       pipe(_lowerCamelCase					,  '''anime turle'''					,  generator=_lowerCamelCase					,  output_type='''np'''    )

       a						:List[Any] 							=       output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''    )

       a						:Optional[Any] 							=       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''    )

       a						:List[str] 							=       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-h-img2img'''					,  torch_dtype=torch.floataa    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       a						:Tuple 							=       torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a						:Optional[int] 							=       pipe(_lowerCamelCase					,  '''anime turle'''					,  generator=_lowerCamelCase					,  output_type='''np'''    )

       a						:int 							=       output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''    )

       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       a						:int 							=       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-h-img2img'''					,  torch_dtype=torch.floataa    )
       a						:Any 							=       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       a						:str 							=       pipe(
           _lowerCamelCase					,  '''anime turtle'''					,  num_inference_steps=2					,  output_type='''np'''					,  )

       a						:List[str] 							=       torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9








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_					)


