




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


UpperCAmelCase_		:					Dict          =  False




class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :str			):
										'''simple docstring'''

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

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
										return tokenizer

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Optional[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=10_00     ,			)
										return CLIPTextModel(__snake_case			)

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[int]										=12
										__magic_name__						:						str										=12

										__magic_name__						:						Optional[int]										={
										    """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""",
										}

										__magic_name__						:						int										=TransformeraDModel(**__snake_case			)
										return model

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Any										="""cpu"""

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

										__magic_name__						:						List[str]										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						int										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

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

										__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Optional[Any]										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						Any										=output.images

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

										__magic_name__						:						List[str]										=image[0, -3:, -3:, -1]
										__magic_name__						:						int										=image_from_tuple[0, -3:, -3:, -1]

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

										__magic_name__						:						Optional[int]										=np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]			)

										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      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										="""cpu"""

										__magic_name__						:						Union[str, Any]										=self.dummy_vqvae
										__magic_name__						:						str										=self.dummy_text_encoder
										__magic_name__						:						Dict										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_transformer
										__magic_name__						:						Tuple										=VQDiffusionScheduler(self.num_embed			)
										__magic_name__						:						int										=LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__snake_case     ,			hidden_size=self.text_embedder_hidden_size     ,			length=tokenizer.model_max_length			)

										__magic_name__						:						int										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						Dict										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Optional[Any]										="""teddy bear playing in the pool"""

										__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Tuple										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						List[str]										=output.images

										__magic_name__						:						Optional[Any]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						str										=pipe(
										    [prompt]     ,			generator=__snake_case     ,			output_type="""np"""     ,			return_dict=__snake_case     ,			num_inference_steps=2			)[0]

										__magic_name__						:						Any										=image[0, -3:, -3:, -1]
										__magic_name__						:						Tuple										=image_from_tuple[0, -3:, -3:, -1]

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

										__magic_name__						:						Union[str, Any]										=np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]			)

										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 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

										__magic_name__						:						int										=VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
										__magic_name__						:						Tuple										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

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

										__magic_name__						:						Dict										=output.images[0]

										assert image.shape == (2_56, 2_56, 3)
										assert np.abs(expected_image - image			).max() < 2.0










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




