


# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCAmelCase      =   """pytorch_model.bin"""
UpperCAmelCase      =   """pytorch_model.bin.index.json"""
UpperCAmelCase      =   """adapter_config.json"""
UpperCAmelCase      =   """adapter_model.bin"""
UpperCAmelCase      =   """adapter_model.safetensors"""
UpperCAmelCase      =   """tf_model.h5"""
UpperCAmelCase      =   """tf_model.h5.index.json"""
UpperCAmelCase      =   """model.ckpt"""
UpperCAmelCase      =   """flax_model.msgpack"""
UpperCAmelCase      =   """flax_model.msgpack.index.json"""
UpperCAmelCase      =   """model.safetensors"""
UpperCAmelCase      =   """model.safetensors.index.json"""
UpperCAmelCase      =   """config.json"""
UpperCAmelCase      =   """preprocessor_config.json"""
UpperCAmelCase      =   FEATURE_EXTRACTOR_NAME
UpperCAmelCase      =   """generation_config.json"""
UpperCAmelCase      =   """modelcard.json"""

UpperCAmelCase      =   """▁"""
UpperCAmelCase      =   SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCAmelCase      =   [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCAmelCase      =   [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCAmelCase      =   [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE ) < version.parse(SCREAMING_SNAKE_CASE ):
            if "dev" in min_version:
                  snake_case_	=				(
                      '''This example requires a source install from HuggingFace Transformers (see '''
                      '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_	=				f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(
                error_message
                + '''Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '''
                '''versions of HuggingFace Transformers.''' )



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()
       snake_case_	=				value_function
       snake_case_	=				unet
       snake_case_	=				scheduler
       snake_case_	=				env
       snake_case_	=				env.get_dataset()
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].mean()
             except:  # noqa: E722
                   pass
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].std()
             except:  # noqa: E722
                   pass
       snake_case_	=				env.observation_space.shape[0]
       snake_case_	=				env.action_space.shape[0]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if type(_UpperCAmelCase ) is dict:
             return {k: self.to_torch(_UpperCAmelCase ) for k, v in x_in.items()}
       elif torch.is_tensor(_UpperCAmelCase ):
             return x_in.to(self.unet.device )
       return torch.tensor(_UpperCAmelCase		,				device=self.unet.device )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				x.shape[0]
       snake_case_	=				None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             snake_case_	=				torch.full((batch_size,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

             # TODO: verify deprecation of this kwarg
             snake_case_	=				self.scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				predict_epsilon=_UpperCAmelCase )['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
             snake_case_	=				self.to_torch(_UpperCAmelCase )
       return x, y





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

       snake_case_	=				{0: self.to_torch(_UpperCAmelCase )}
       snake_case_	=				(batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       snake_case_	=				randn_tensor(_UpperCAmelCase		,				device=self.unet.device )
       snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
       snake_case_	=				self.to_torch(_UpperCAmelCase )

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # sort output trajectories by value
       snake_case_	=				y.argsort(0		,				descending=_UpperCAmelCase ).squeeze()
       snake_case_	=				x[sorted_idx]
       snake_case_	=				sorted_values[:, :, : self.action_dim]
       snake_case_	=				actions.detach().cpu().numpy()
       snake_case_	=				self.de_normalize(_UpperCAmelCase		,				key='''actions''' )

       # select the action with the highest value
       if y is not None:
             snake_case_	=				0
       else:
             # if we didn't run value guiding, select a random action
             snake_case_	=				np.random.randint(0		,				_UpperCAmelCase )

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions