


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
      import tensorflow as tf


SCREAMING_SNAKE_CASE       :		Dict         =				logging.get_logger(__name__)






@dataclass
class      UpperCamelCase					(		_UpperCAmelCase     ):


   '''simple docstring'''







   lowercase					:	List[str]									=[
       """no_inference""",
       """no_cuda""",
       """no_tpu""",
       """no_speed""",
       """no_memory""",
       """no_env_print""",
       """no_multi_process""",
   ]
   def __init__(     self   ,							**UpperCamelCase_		):






        for deprecated_arg in self.deprecated_args:
             if deprecated_arg in kwargs:
                  lowercase_       :Optional[int]     =       deprecated_arg[3:]
                  lowercase_       :Union[str, Any]     =       not kwargs.pop(lowercase__		)
                  logger.warning(
                      f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                      f" {positive_arg}={kwargs[positive_arg]}"		)
        lowercase_       :List[Any]     =       kwargs.pop('''tpu_name'''   ,							self.tpu_name		)
        lowercase_       :Optional[int]     =       kwargs.pop('''device_idx'''   ,							self.device_idx		)
        lowercase_       :int     =       kwargs.pop('''eager_mode'''   ,							self.eager_mode		)
        lowercase_       :str     =       kwargs.pop('''use_xla'''   ,							self.use_xla		)
        super().__init__(**lowercase__		)

   lowercase					:	Optional[Any]									=field(
       default=_UpperCAmelCase						,      metadata={"""help""": """Name of TPU"""}						,      )
   lowercase					:	Optional[Any]									=field(
       default=0						,      metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}						,      )
   lowercase					:	Dict									=field(default=_UpperCAmelCase						,      metadata={"""help""": """Benchmark models in eager model."""}     )
   lowercase					:	Any									=field(
       default=_UpperCAmelCase						,      metadata={
           """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
       }						,      )
   @cached_property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        lowercase_       :Optional[Any]     =       None
        if self.tpu:
             try:
                  if self.tpu_name:
                       lowercase_       :Optional[int]     =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
                  else:
                       lowercase_       :Union[str, Any]     =       tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                  lowercase_       :str     =       None
        return tpu
   @cached_property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        if self.is_tpu:
             tf.config.experimental_connect_to_cluster(self._setup_tpu		)
             tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

             lowercase_       :Any     =       tf.distribute.TPUStrategy(self._setup_tpu		)
        else:
             # currently no multi gpu is allowed
             if self.is_gpu:
                  # TODO: Currently only single GPU is supported
                  tf.config.set_visible_devices(self.gpu_list[self.device_idx]   ,							'''GPU'''		)
                  lowercase_       :Dict     =       tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"		)
             else:
                  tf.config.set_visible_devices([]   ,							'''GPU'''		)  # disable GPU
                  lowercase_       :Optional[Any]     =       tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"		)

        return strategy
   @property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        return self._setup_tpu is not None
   @property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        return self._setup_strategy
   @property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        return tf.config.list_physical_devices('''GPU'''		)
   @property
   def 		UpperCamelCase  (     self		):
        requires_backends(self   ,							['''tf''']		)
        if self.cuda:
             return len(self.gpu_list		)
        return 0




   @property
   def 		UpperCamelCase  (     self		):
        return self.n_gpu > 0





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       :		int         =				False






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   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		)
        lowercase_       :List[Any]     =       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		):
        lowercase_       :Optional[Any]     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
        return tokenizer
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :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(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)

        lowercase_       :int     =       12
        lowercase_       :List[Any]     =       12

        lowercase_       :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''',
        }

        lowercase_       :int     =       TransformeraDModel(**UpperCamelCase_		)
        return model
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''

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

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

        lowercase_       :Dict     =       '''teddy bear playing in the pool'''

        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Any     =       output.images

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

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

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

        lowercase_       :str     =       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 		UpperCamelCase  (     self		):
        lowercase_       :int     =       '''cpu'''

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

        lowercase_       :Optional[int]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Dict     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :int     =       '''teddy bear playing in the pool'''

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Optional[Any]     =       output.images

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

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

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

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




   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''		)

        lowercase_       :int     =       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''		)
        lowercase_       :Tuple     =       pipeline.to(UpperCamelCase_		)
        pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

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

        lowercase_       :List[str]     =       output.images[0]

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

