
import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_snake_case      :							Union[str, Any]     	=    False





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[str]:
										return 12





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->int:
										return 12





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										return 32





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Optional[int]:
										torch.manual_seed(0     )
										lowercase         =      VQModel(
										    block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,    latent_channels=3		,    num_vq_embeddings=self.num_embed		,    vq_embed_dim=3		,    )
										return model





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Optional[Any]:
										lowercase         =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )
										return tokenizer





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Union[str, Any]:
										torch.manual_seed(0     )
										lowercase         =      CLIPTextConfig(
										    bos_token_id=0		,    eos_token_id=2		,    hidden_size=self.text_embedder_hidden_size		,    intermediate_size=37		,    layer_norm_eps=1E-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    )
										return CLIPTextModel(lowerCAmelCase__     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Any:
										torch.manual_seed(0     )

										lowercase         =      12
										lowercase         =      12

										lowercase         =      {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										lowercase         =      TransformeraDModel(**lowerCAmelCase__     )
										return model





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Union[str, Any]:
										lowercase         =      "cpu"

										lowercase         =      self.dummy_vqvae
										lowercase         =      self.dummy_text_encoder
										lowercase         =      self.dummy_tokenizer
										lowercase         =      self.dummy_transformer
										lowercase         =      VQDiffusionScheduler(self.num_embed     )
										lowercase         =      LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__     )

										lowercase         =      VQDiffusionPipeline(
										    vqvae=lowerCAmelCase__		,    text_encoder=lowerCAmelCase__		,    tokenizer=lowerCAmelCase__		,    transformer=lowerCAmelCase__		,    scheduler=lowerCAmelCase__		,    learned_classifier_free_sampling_embeddings=lowerCAmelCase__		,    )
										lowercase         =      pipe.to(lowerCAmelCase__     )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

										lowercase         =      "teddy bear playing in the pool"

										lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
										lowercase         =      pipe([prompt]		,    generator=lowerCAmelCase__		,    num_inference_steps=2		,    output_type="np"     )
										lowercase         =      output.images

										lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
										lowercase         =      pipe(
										    [prompt]		,    generator=lowerCAmelCase__		,    output_type="np"		,    return_dict=lowerCAmelCase__		,    num_inference_steps=2     )[0]

										lowercase         =      image[0, -3:, -3:, -1]
										lowercase         =      image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										lowercase         =      np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]     )

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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[Any]:
										lowercase         =      "cpu"

										lowercase         =      self.dummy_vqvae
										lowercase         =      self.dummy_text_encoder
										lowercase         =      self.dummy_tokenizer
										lowercase         =      self.dummy_transformer
										lowercase         =      VQDiffusionScheduler(self.num_embed     )
										lowercase         =      LearnedClassifierFreeSamplingEmbeddings(
										    learnable=lowerCAmelCase__		,    hidden_size=self.text_embedder_hidden_size		,    length=tokenizer.model_max_length     )

										lowercase         =      VQDiffusionPipeline(
										    vqvae=lowerCAmelCase__		,    text_encoder=lowerCAmelCase__		,    tokenizer=lowerCAmelCase__		,    transformer=lowerCAmelCase__		,    scheduler=lowerCAmelCase__		,    learned_classifier_free_sampling_embeddings=lowerCAmelCase__		,    )
										lowercase         =      pipe.to(lowerCAmelCase__     )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

										lowercase         =      "teddy bear playing in the pool"

										lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
										lowercase         =      pipe([prompt]		,    generator=lowerCAmelCase__		,    num_inference_steps=2		,    output_type="np"     )
										lowercase         =      output.images

										lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
										lowercase         =      pipe(
										    [prompt]		,    generator=lowerCAmelCase__		,    output_type="np"		,    return_dict=lowerCAmelCase__		,    num_inference_steps=2     )[0]

										lowercase         =      image[0, -3:, -3:, -1]
										lowercase         =      image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										lowercase         =      np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]     )

										assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2





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







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										lowercase         =      load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"     )

										lowercase         =      VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"     )
										lowercase         =      pipeline.to(lowerCAmelCase__     )
										pipeline.set_progress_bar_config(disable=lowerCAmelCase__     )

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										lowercase         =      torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
										lowercase         =      pipeline(
										    "teddy bear playing in the pool"		,    num_images_per_prompt=1		,    generator=lowerCAmelCase__		,    output_type="np"		,    )

										lowercase         =      output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image     ).max() < 2.0


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							List[str]     	=    {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					'''deberta-v2'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Union[str, Any]=128100		,    lowerCAmelCase__							:Optional[Any]=1536		,    lowerCAmelCase__							:Tuple=24		,    lowerCAmelCase__							:List[Any]=24		,    lowerCAmelCase__							:Union[str, Any]=6144		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:List[str]=512		,    lowerCAmelCase__							:Optional[int]=0		,    lowerCAmelCase__							:Optional[int]=0.02		,    lowerCAmelCase__							:int=1E-7		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:List[Any]=-1		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[Any]=0		,    lowerCAmelCase__							:Union[str, Any]="gelu"		,    **lowerCAmelCase__							:str		,    )  ->Optional[Any]:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      initializer_range
										lowercase         =      relative_attention
										lowercase         =      max_relative_positions
										lowercase         =      pad_token_id
										lowercase         =      position_biased_input

										# Backwards compatibility
										if type(lowerCAmelCase__     ) == str:
														lowercase         =      [x.strip() for x in pos_att_type.lower().split("|"     )]

										lowercase         =      pos_att_type
										lowercase         =      vocab_size
										lowercase         =      layer_norm_eps

										lowercase         =      kwargs.get("pooler_hidden_size"		,    lowerCAmelCase__     )
										lowercase         =      pooler_dropout
										lowercase         =      pooler_hidden_act





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowercase         =      {0: "batch", 1: "choice", 2: "sequence"}
										else:
														lowercase         =      {0: "batch", 1: "sequence"}
										if self._config.type_vocab_size > 0:
														return OrderedDict(
														    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]     )
										else:
														return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return 12





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional["TensorType"] = None		,    lowerCAmelCase__							:int = 3		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:"PreTrainedTokenizerBase" = None		,    )  ->Mapping[str, Any]:
										lowercase         =      super().generate_dummy_inputs(preprocessor=lowerCAmelCase__		,    framework=lowerCAmelCase__     )
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
														del dummy_inputs["token_type_ids"]
										return dummy_inputs
