






import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.framework.errors_impl import ResourceExhaustedError

 from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
 import pyanvml.pyanvml as nvml

__lowerCamelCase	:		str										=   logging.get_logger(__name__)







def 			A__							(      _a  :					bool		,  _a  :					bool	):





 '''simple docstring'''







 def run_func(_a  :					int	):
  @wraps(_a	)
  def run_in_eager_mode(*_a  :					List[str]		,  **_a  :					Optional[Any]	):
   return func(*_a		,  **_a	)

  @wraps(_a	)
  @tf.function(experimental_compile=_a	)
  def run_in_graph_mode(*_a  :					int		,  **_a  :					Optional[int]	):
   return func(*_a		,  **_a	)

  if do_eager_mode is True:
   if use_xla is not False:
    raise ValueError(
        """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""	)
   return run_in_eager_mode
  else:
   return run_in_graph_mode

 return run_func







def 			A__							(      _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =random.Random()
 snake_case__  :    List[Any]							       =[rng.randint(0		,  vocab_size - 1	) for i in range(batch_size * sequence_length	)]
 return tf.constant(_a		,  shape=(batch_size, sequence_length)		,  dtype=tf.intaa	)







class  _lowercase   (   _A						):
      _a							:						TensorFlowBenchmarkArguments
      _a							:						PretrainedConfig
      _a							:						str										=							"TensorFlow"
      @property
      def 						lowercase__					(     self   ):
       return tf.__version__
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       # initialize GPU on separate process
       snake_case__  :    Optional[Any]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Union[str, Any]							       =self._prepare_inference_func(a				,			a				,			a   )
       return self._measure_speed(_inference   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Union[str, Any]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Optional[int]							       =self._prepare_train_func(a				,			a				,			a   )
       return self._measure_speed(_train   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       # initialize GPU on separate process
       if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,			a   )
       snake_case__  :    Tuple							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Union[str, Any]							       =self._prepare_inference_func(a				,			a				,			a   )
       return self._measure_memory(_inference   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,			a   )
       snake_case__  :    List[str]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )

       snake_case__  :    Any							       =self._prepare_train_func(a				,			a				,			a   )
       return self._measure_memory(_train   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =self.config_dict[model_name]

       if self.args.fpaa:
        raise NotImplementedError("""Mixed precision is currently not supported."""   )

       snake_case__  :    str							       =(
           hasattr(a				,			"""architectures"""   )
           and isinstance(config.architectures				,			a   )
           and len(config.architectures   ) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
         snake_case__  :    Dict							       ="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
         snake_case__  :    int							       =__import__("""transformers"""				,			fromlist=[model_class]   )
         snake_case__  :    str							       =getattr(a				,			a   )
         snake_case__  :    Optional[int]							       =model_cls(a   )
        except ImportError:
         raise ImportError(
             F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
             """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""   )
       else:
        snake_case__  :    Tuple							       =TF_MODEL_MAPPING[config.__class__](a   )

       # encoder-decoder has vocab size saved differently
       snake_case__  :    Optional[Any]							       =config.vocab_size if hasattr(a				,			"""vocab_size"""   ) else config.encoder.vocab_size
       snake_case__  :    Dict							       =random_input_ids(a				,			a				,			a   )

       @run_with_tf_optimizations(self.args.eager_mode				,			self.args.use_xla   )
       def encoder_decoder_forward():
        return model(a				,			decoder_input_ids=a				,			training=a   )

       @run_with_tf_optimizations(self.args.eager_mode				,			self.args.use_xla   )
       def encoder_forward():
        return model(a				,			training=a   )

       snake_case__  :    Tuple							       =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =self.config_dict[model_name]

       if self.args.eager_mode is not False:
        raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""   )

       if self.args.fpaa:
        raise NotImplementedError("""Mixed precision is currently not supported."""   )

       snake_case__  :    Optional[Any]							       =(
           hasattr(a				,			"""architectures"""   )
           and isinstance(config.architectures				,			a   )
           and len(config.architectures   ) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
         snake_case__  :    Dict							       ="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
         snake_case__  :    List[str]							       =__import__("""transformers"""				,			fromlist=[model_class]   )
         snake_case__  :    List[str]							       =getattr(a				,			a   )
         snake_case__  :    Union[str, Any]							       =model_cls(a   )
        except ImportError:
         raise ImportError(
             F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
             """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""   )
       else:
        snake_case__  :    Any							       =TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a   )

       # encoder-decoder has vocab size saved differently
       snake_case__  :    int							       =config.vocab_size if hasattr(a				,			"""vocab_size"""   ) else config.encoder.vocab_size
       snake_case__  :    Union[str, Any]							       =random_input_ids(a				,			a				,			a   )

       @run_with_tf_optimizations(self.args.eager_mode				,			self.args.use_xla   )
       def encoder_decoder_train():
        snake_case__  :    int							       =model(a				,			decoder_input_ids=a				,			labels=a				,			training=a   )[0]
        snake_case__  :    str							       =tf.gradients(a				,			model.trainable_variables   )
        return gradients

       @run_with_tf_optimizations(self.args.eager_mode				,			self.args.use_xla   )
       def encoder_train():
        snake_case__  :    Any							       =model(a				,			labels=a				,			training=a   )[0]
        snake_case__  :    Optional[Any]							       =tf.gradients(a				,			model.trainable_variables   )
        return gradients

       snake_case__  :    List[str]							       =encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train
      def 						lowercase__					(     self				,			a   ):
       with self.args.strategy.scope():
        try:
         if self.args.is_tpu or self.args.use_xla:
          # run additional 10 times to stabilize compilation for tpu
          logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""   )
          timeit.repeat(a				,			repeat=1				,			number=5   )

         # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
         snake_case__  :    Dict							       =timeit.repeat(
             a				,			repeat=self.args.repeat				,			number=1_0				,			)

         return min(a   ) / 10.0
        except ResourceExhaustedError as e:
         self.print_fn(F"Doesn't fit on GPU. {e}"   )



      def 						lowercase__					(     self				,			a   ):
       logger.info(
           """Note that TensorFlow allocates more memory than """
           """it might need to speed up computation. """
           """The memory reported here corresponds to the memory """
           """reported by `nvidia-smi`, which can vary depending """
           """on total available memory on the GPU that is used."""   )
       with self.args.strategy.scope():
        try:
         if self.args.trace_memory_line_by_line:
          if not self.args.eager_mode:
           raise ValueError(
               """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
               """ consumption line by line."""   )
          snake_case__  :    Tuple							       =start_memory_tracing("""transformers"""   )

         if self.args.is_tpu:
          # tpu
          raise NotImplementedError(
              """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
              """ with `args.memory=False`"""   )
         elif self.args.is_gpu:
          # gpu
          if not is_pyanvml_available():
           logger.warning(
               """py3nvml not installed, we won't log GPU memory usage. """
               """Install py3nvml (pip install py3nvml) to log information about GPU."""   )
           snake_case__  :    Tuple							       ="""N/A"""
          else:
           logger.info(
               """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
               """ running on the same GPU."""   )
           # init nvml
           nvml.nvmlInit()
           func()
           snake_case__  :    Dict							       =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
           snake_case__  :    Tuple							       =nvml.nvmlDeviceGetMemoryInfo(a   )
           snake_case__  :    Dict							       =meminfo.used
           snake_case__  :    List[Any]							       =Memory(a   )
           # shutdown nvml
           nvml.nvmlShutdown()
         else:
          # cpu
          if self.args.trace_memory_line_by_line:
           logger.info(
               """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
               """ TensorFlow."""   )
           snake_case__  :    Dict							       =None
          else:
           snake_case__  :    Union[str, Any]							       =measure_peak_memory_cpu(a   )
           snake_case__  :    int							       =Memory(a   ) if isinstance(a				,			a   ) else memory_bytes
         if self.args.trace_memory_line_by_line:
          snake_case__  :    Tuple							       =stop_memory_tracing(a   )
          if memory is None:
           snake_case__  :    Any							       =summary.total
         else:
          snake_case__  :    List[Any]							       =None

         return memory, summary
        except ResourceExhaustedError as e:
         self.print_fn(F"Doesn't fit on GPU. {e}"   )
         return "N/A", None













import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Any										=							KandinskyImgaImgPipeline
      _a							:						List[Any]										=							['prompt', 'image_embeds', 'negative_image_embeds', 'image']
      _a							:						int										=							[
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
      ]
      _a							:						int										=							[
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _a							:						List[str]										=							False
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim * 4
      @property
      def 						lowercase__					(     self   ):
       return 1_0_0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""   )
       return tokenizer
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Optional[Any]							       =MCLIPConfig(
           numDims=self.cross_attention_dim				,			transformerDimensions=self.text_embedder_hidden_size				,			hidden_size=self.text_embedder_hidden_size				,			intermediate_size=3_7				,			num_attention_heads=4				,			num_hidden_layers=5				,			vocab_size=1_0_0_5				,			)

       snake_case__  :    Optional[int]							       =MultilingualCLIP(a   )
       snake_case__  :    Union[str, Any]							       =text_encoder.eval()

       return text_encoder
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    Optional[int]							       ={
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       snake_case__  :    List[str]							       =UNetaDConditionModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    List[Any]							       =VQModel(**self.dummy_movq_kwargs   )
       return model
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =self.dummy_text_encoder
       snake_case__  :    Tuple							       =self.dummy_tokenizer
       snake_case__  :    List[str]							       =self.dummy_unet
       snake_case__  :    Dict							       =self.dummy_movq

       snake_case__  :    Union[str, Any]							       ={
           """num_train_timesteps""": 1_0_0_0,
           """beta_schedule""": """linear""",
           """beta_start""": 0.00085,
           """beta_end""": 0.012,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       snake_case__  :    Union[str, Any]							       =DDIMScheduler(**a   )

       snake_case__  :    Any							       ={
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components
      def 						lowercase__					(     self				,			a				,			a=0   ):
       snake_case__  :    str							       =floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(a   )   ).to(a   )
       snake_case__  :    Tuple							       =floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(seed + 1   )   ).to(a   )
       # create init_image
       snake_case__  :    str							       =floats_tensor((1, 3, 6_4, 6_4)				,			rng=random.Random(a   )   ).to(a   )
       snake_case__  :    Tuple							       =image.cpu().permute(0				,			2				,			3				,			1   )[0]
       snake_case__  :    Dict							       =Image.fromarray(np.uinta(a   )   ).convert("""RGB"""   ).resize((2_5_6, 2_5_6)   )

       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    Dict							       =torch.manual_seed(a   )
       else:
        snake_case__  :    Optional[Any]							       =torch.Generator(device=a   ).manual_seed(a   )
       snake_case__  :    str							       ={
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 6_4,
           """width""": 6_4,
           """num_inference_steps""": 1_0,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""

       snake_case__  :    Tuple							       =self.get_dummy_components()

       snake_case__  :    Optional[int]							       =self.pipeline_class(**a   )
       snake_case__  :    Union[str, Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =pipe(**self.get_dummy_inputs(a   )   )
       snake_case__  :    List[str]							       =output.images

       snake_case__  :    int							       =pipe(
           **self.get_dummy_inputs(a   )				,			return_dict=a				,			)[0]

       snake_case__  :    str							       =image[0, -3:, -3:, -1]
       snake_case__  :    Tuple							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    List[str]							       =np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]   )
       assert (
           np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""   )

       snake_case__  :    Dict							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
       snake_case__  :    Any							       ="""A red cartoon frog, 4k"""

       snake_case__  :    Dict							       =KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,			torch_dtype=torch.floataa   )
       pipe_prior.to(a   )

       snake_case__  :    Tuple							       =KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,			torch_dtype=torch.floataa   )
       snake_case__  :    str							       =pipeline.to(a   )

       pipeline.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       =torch.Generator(device="""cpu"""   ).manual_seed(0   )
       snake_case__       ,   snake_case__  :    Any							       =pipe_prior(
           a				,			generator=a				,			num_inference_steps=5				,			negative_prompt=""""""				,			).to_tuple()

       snake_case__  :    str							       =pipeline(
           a				,			image=a				,			image_embeds=a				,			negative_image_embeds=a				,			generator=a				,			num_inference_steps=1_0_0				,			height=7_6_8				,			width=7_6_8				,			strength=0.2				,			output_type="""np"""				,			)

       snake_case__  :    Optional[int]							       =output.images[0]

       assert image.shape == (7_6_8, 7_6_8, 3)

       assert_mean_pixel_difference(a				,			a   )





