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


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			False



class        lowercase_   (					unittest.TestCase		):



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



							@property
							def 	UpperCamelCase	(						self    ):
													return 12



							@property
							def 	UpperCamelCase	(						self    ):
													return 12



							@property
							def 	UpperCamelCase	(						self    ):
													return 32



							@property
							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					Dict    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
													return tokenizer



							@property
							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					int    =   CLIPTextConfig(
													    bos_token_id=0				,    eos_token_id=2				,    hidden_size=self.text_embedder_hidden_size				,    intermediate_size=37				,    layer_norm_eps=1e-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1_000				,    )
													return CLIPTextModel(lowercase_    )



							@property
							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )

													_snake_case						:					Dict    =   12
													_snake_case						:					int    =   12

													_snake_case						:					Tuple    =   {
													    "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",
													}

													_snake_case						:					Optional[Any]    =   TransformeraDModel(**lowercase_    )
													return model



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   "cpu"

													_snake_case						:					str    =   self.dummy_vqvae
													_snake_case						:					Tuple    =   self.dummy_text_encoder
													_snake_case						:					List[str]    =   self.dummy_tokenizer
													_snake_case						:					List[Any]    =   self.dummy_transformer
													_snake_case						:					Optional[Any]    =   VQDiffusionScheduler(self.num_embed    )
													_snake_case						:					Optional[Any]    =   LearnedClassifierFreeSamplingEmbeddings(learnable=lowercase_    )

													_snake_case						:					int    =   VQDiffusionPipeline(
													    vqvae=lowercase_				,    text_encoder=lowercase_				,    tokenizer=lowercase_				,    transformer=lowercase_				,    scheduler=lowercase_				,    learned_classifier_free_sampling_embeddings=lowercase_				,    )
													_snake_case						:					List[Any]    =   pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Optional[int]    =   "teddy bear playing in the pool"

													_snake_case						:					int    =   torch.Generator(device=lowercase_    ).manual_seed(0    )
													_snake_case						:					str    =   pipe([prompt]				,    generator=lowercase_				,    num_inference_steps=2				,    output_type="np"    )
													_snake_case						:					Dict    =   output.images

													_snake_case						:					List[str]    =   torch.Generator(device=lowercase_    ).manual_seed(0    )
													_snake_case						:					Tuple    =   pipe(
													    [prompt]				,    generator=lowercase_				,    output_type="np"				,    return_dict=lowercase_				,    num_inference_steps=2    )[0]

													_snake_case						:					Union[str, Any]    =   image[0, -3:, -3:, -1]
													_snake_case						:					List[str]    =   image_from_tuple[0, -3:, -3:, -1]

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

													_snake_case						:					Optional[Any]    =   np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]    )

													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 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   "cpu"

													_snake_case						:					str    =   self.dummy_vqvae
													_snake_case						:					List[Any]    =   self.dummy_text_encoder
													_snake_case						:					Union[str, Any]    =   self.dummy_tokenizer
													_snake_case						:					str    =   self.dummy_transformer
													_snake_case						:					int    =   VQDiffusionScheduler(self.num_embed    )
													_snake_case						:					List[str]    =   LearnedClassifierFreeSamplingEmbeddings(
													    learnable=lowercase_				,    hidden_size=self.text_embedder_hidden_size				,    length=tokenizer.model_max_length    )

													_snake_case						:					str    =   VQDiffusionPipeline(
													    vqvae=lowercase_				,    text_encoder=lowercase_				,    tokenizer=lowercase_				,    transformer=lowercase_				,    scheduler=lowercase_				,    learned_classifier_free_sampling_embeddings=lowercase_				,    )
													_snake_case						:					Union[str, Any]    =   pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Any    =   "teddy bear playing in the pool"

													_snake_case						:					Dict    =   torch.Generator(device=lowercase_    ).manual_seed(0    )
													_snake_case						:					List[Any]    =   pipe([prompt]				,    generator=lowercase_				,    num_inference_steps=2				,    output_type="np"    )
													_snake_case						:					Dict    =   output.images

													_snake_case						:					int    =   torch.Generator(device=lowercase_    ).manual_seed(0    )
													_snake_case						:					Dict    =   pipe(
													    [prompt]				,    generator=lowercase_				,    output_type="np"				,    return_dict=lowercase_				,    num_inference_steps=2    )[0]

													_snake_case						:					Dict    =   image[0, -3:, -3:, -1]
													_snake_case						:					Any    =   image_from_tuple[0, -3:, -3:, -1]

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

													_snake_case						:					str    =   np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]    )

													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        lowercase_   (					unittest.TestCase		):



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"    )

													_snake_case						:					List[Any]    =   VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"    )
													_snake_case						:					int    =   pipeline.to(lowercase_    )
													pipeline.set_progress_bar_config(disable=lowercase_    )

													# requires GPU generator for gumbel softmax
													# don't use GPU generator in tests though
													_snake_case						:					Optional[Any]    =   torch.Generator(device=lowercase_    ).manual_seed(0    )
													_snake_case						:					List[Any]    =   pipeline(
													    "teddy bear playing in the pool"				,    num_images_per_prompt=1				,    generator=lowercase_				,    output_type="np"				,    )

													_snake_case						:					Union[str, Any]    =   output.images[0]

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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)