


'''simple docstring'''







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


lowerCAmelCase    :     str					=						False
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     @property
     def a	( self   ):

            '''simple docstring'''

            return 12







     @property
     def a	( self   ):

            '''simple docstring'''

            return 12







     @property
     def a	( self   ):

            '''simple docstring'''

            return 32







     @property
     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							int   			=       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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
            return tokenizer







     @property
     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							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=1000      ,	)
            return CLIPTextModel(snake_case__   )







     @property
     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )

            _lowerCAmelCase    :							Any   			=       12
            _lowerCAmelCase    :							Any   			=       12

            _lowerCAmelCase    :							Dict   			=       {
                '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',
            }

            _lowerCAmelCase    :							str   			=       TransformeraDModel(**snake_case__   )
            return model







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       'cpu'

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

            _lowerCAmelCase    :							Tuple   			=       VQDiffusionPipeline(
                vqvae=snake_case__      ,	text_encoder=snake_case__      ,	tokenizer=snake_case__      ,	transformer=snake_case__      ,	scheduler=snake_case__      ,	learned_classifier_free_sampling_embeddings=snake_case__      ,	)
            _lowerCAmelCase    :							Dict   			=       pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       'teddy bear playing in the pool'

            _lowerCAmelCase    :							List[str]   			=       torch.Generator(device=snake_case__   ).manual_seed(0   )
            _lowerCAmelCase    :							List[str]   			=       pipe([prompt]      ,	generator=snake_case__      ,	num_inference_steps=2      ,	output_type='np'   )
            _lowerCAmelCase    :							str   			=       output.images

            _lowerCAmelCase    :							Union[str, Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(0   )
            _lowerCAmelCase    :							Union[str, Any]   			=       pipe(
                [prompt]      ,	generator=snake_case__      ,	output_type='np'      ,	return_dict=snake_case__      ,	num_inference_steps=2   )[0]

            _lowerCAmelCase    :							int   			=       image[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Tuple   			=       image_from_tuple[0, -3:, -3:, -1]

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

            _lowerCAmelCase    :							Tuple   			=       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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       'cpu'

            _lowerCAmelCase    :							Tuple   			=       self.dummy_vqvae
            _lowerCAmelCase    :							List[Any]   			=       self.dummy_text_encoder
            _lowerCAmelCase    :							Optional[int]   			=       self.dummy_tokenizer
            _lowerCAmelCase    :							Any   			=       self.dummy_transformer
            _lowerCAmelCase    :							Dict   			=       VQDiffusionScheduler(self.num_embed   )
            _lowerCAmelCase    :							Tuple   			=       LearnedClassifierFreeSamplingEmbeddings(
                learnable=snake_case__      ,	hidden_size=self.text_embedder_hidden_size      ,	length=tokenizer.model_max_length   )

            _lowerCAmelCase    :							Tuple   			=       VQDiffusionPipeline(
                vqvae=snake_case__      ,	text_encoder=snake_case__      ,	tokenizer=snake_case__      ,	transformer=snake_case__      ,	scheduler=snake_case__      ,	learned_classifier_free_sampling_embeddings=snake_case__      ,	)
            _lowerCAmelCase    :							Optional[Any]   			=       pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       'teddy bear playing in the pool'

            _lowerCAmelCase    :							Union[str, Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(0   )
            _lowerCAmelCase    :							str   			=       pipe([prompt]      ,	generator=snake_case__      ,	num_inference_steps=2      ,	output_type='np'   )
            _lowerCAmelCase    :							int   			=       output.images

            _lowerCAmelCase    :							Dict   			=       torch.Generator(device=snake_case__   ).manual_seed(0   )
            _lowerCAmelCase    :							Optional[Any]   			=       pipe(
                [prompt]      ,	generator=snake_case__      ,	output_type='np'      ,	return_dict=snake_case__      ,	num_inference_steps=2   )[0]

            _lowerCAmelCase    :							str   			=       image[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Optional[int]   			=       image_from_tuple[0, -3:, -3:, -1]

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

            _lowerCAmelCase    :							List[str]   			=       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 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							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'   )

            _lowerCAmelCase    :							Tuple   			=       VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'   )
            _lowerCAmelCase    :							Any   			=       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
            _lowerCAmelCase    :							Optional[int]   			=       torch.Generator(device=snake_case__   ).manual_seed(0   )
            _lowerCAmelCase    :							str   			=       pipeline(
                'teddy bear playing in the pool'      ,	num_images_per_prompt=1      ,	generator=snake_case__      ,	output_type='np'      ,	)

            _lowerCAmelCase    :							int   			=       output.images[0]

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





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

