
# 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,
)


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

__lowerCamelCase         =			'''▁'''
__lowerCamelCase         =			SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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







def    _snake_case (       __snake_case    )     ->     Dict:






      '''simple docstring'''







      if version.parse(__snake_case    ) < version.parse(__snake_case    ):
            if "dev" in min_version:
                  UpperCAmelCase_  :	int				      =		(
                      "This example requires a source install from HuggingFace Transformers (see "
                      "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
                  )
            else:
                  UpperCAmelCase_  :	List[str]				      =		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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				AltDiffusionPipeline
       _lowerCamelCase			    =				TEXT_TO_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_BATCH_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS



       def  A_   (     self):

             """simple docstring"""





             torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[Any]				      =		UNetaDConditionModel(
                 block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=32     ,)
             UpperCAmelCase_  :	Any				      =		DDIMScheduler(
                 beta_start=0.0_0085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=lowercase     ,set_alpha_to_one=lowercase     ,)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		AutoencoderKL(
                 block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,latent_channels=4     ,)

             # TODO: address the non-deterministic text encoder (fails for save-load tests)
             # torch.manual_seed(0)
             # text_encoder_config = RobertaSeriesConfig(
             #     hidden_size=32,
             #     project_dim=32,
             #     intermediate_size=37,
             #     layer_norm_eps=1e-05,
             #     num_attention_heads=4,
             #     num_hidden_layers=5,
             #     vocab_size=5002,
             # )
             # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

             torch.manual_seed(0)
             UpperCAmelCase_  :	Tuple				      =		CLIPTextConfig(
                 bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,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=5002     ,)
             UpperCAmelCase_  :	Dict				      =		CLIPTextModel(lowercase)

             UpperCAmelCase_  :	List[Any]				      =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
             UpperCAmelCase_  :	List[str]				      =		77

             UpperCAmelCase_  :	Union[str, Any]				      =		{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components



       def  A_   (     self     ,lowercase     ,lowercase=0):

             """simple docstring"""





             if str(lowercase).startswith("mps"):
                   UpperCAmelCase_  :	Any				      =		torch.manual_seed(lowercase)
             else:
                   UpperCAmelCase_  :	Dict				      =		torch.Generator(device=lowercase).manual_seed(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		{
                 "prompt": "A painting of a squirrel eating a burger",
                 "generator": generator,
                 "num_inference_steps": 2,
                 "guidance_scale": 6.0,
                 "output_type": "numpy",
             }
             return inputs



       def  A_   (     self):

             """simple docstring"""





             super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             super().test_inference_batch_single_identical(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	Any				      =		self.get_dummy_components()
             torch.manual_seed(0)
             UpperCAmelCase_  :	List[Any]				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Any				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		text_encoder

             UpperCAmelCase_  :	List[Any]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Tuple				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		"A photo of an astronaut"
             UpperCAmelCase_  :	str				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	List[str]				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Optional[Any]				      =		np.array(
                 [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	int				      =		self.get_dummy_components()
             UpperCAmelCase_  :	Optional[Any]				      =		PNDMScheduler(skip_prk_steps=lowercase)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Union[str, Any]				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Dict				      =		text_encoder
             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	List[Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	int				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Tuple				      =		np.array(
                 [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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


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




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=lowercase)
             UpperCAmelCase_  :	Any				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Any				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Any				      =		torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[int]				      =		alt_pipe([prompt]     ,generator=lowercase     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np")

             UpperCAmelCase_  :	Dict				      =		output.images

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	Dict				      =		np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=lowercase     ,safety_checker=lowercase)
             UpperCAmelCase_  :	List[str]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Optional[Any]				      =		torch.manual_seed(0)

             UpperCAmelCase_  :	List[str]				      =		alt_pipe([prompt]     ,generator=lowercase     ,num_inference_steps=2     ,output_type="numpy")
             UpperCAmelCase_  :	int				      =		output.images

             UpperCAmelCase_  :	str				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	List[Any]				      =		np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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