



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

__snake_case						:Dict						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    def run_func(_UpperCAmelCase    ):
        @wraps(_UpperCAmelCase    )
        def run_in_eager_mode(*_UpperCAmelCase       ,			**_UpperCAmelCase    ):
            return func(*_UpperCAmelCase       ,			**_UpperCAmelCase    )

        @wraps(_UpperCAmelCase    )
        @tf.function(experimental_compile=_UpperCAmelCase    )
        def run_in_graph_mode(*_UpperCAmelCase       ,			**_UpperCAmelCase    ):
            return func(*_UpperCAmelCase       ,			**_UpperCAmelCase    )

        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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    random.Random()
    __a							=    [rng.randint(0       ,			vocab_size - 1    ) for i in range(batch_size * sequence_length    )]
    return tf.constant(_UpperCAmelCase       ,			shape=(batch_size, sequence_length)       ,			dtype=tf.intaa    )

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   TensorFlowBenchmarkArguments
       UpperCamelCase__  :   PretrainedConfig
       UpperCamelCase__  :   str						 =						"TensorFlow"



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return tf.__version__



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_inference_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_speed(_inference)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_train_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_speed(_train)



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_inference_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_memory(_inference)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')

           __a							=    self._prepare_train_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_memory(_train)



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.config_dict[model_name]

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

           __a							=    (
               hasattr(__SCREAMING_SNAKE_CASE					,		'''architectures''')
               and isinstance(config.architectures					,		__SCREAMING_SNAKE_CASE)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   __a							=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   __a							=    __import__('''transformers'''					,		fromlist=[model_class])
                   __a							=    getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_cls(__SCREAMING_SNAKE_CASE)
               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:
               __a							=    TF_MODEL_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE)

           # encoder-decoder has vocab size saved differently
           __a							=    config.vocab_size if hasattr(__SCREAMING_SNAKE_CASE					,		'''vocab_size''') else config.encoder.vocab_size
           __a							=    random_input_ids(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

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

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

           __a							=    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

           return _inference



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    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.''')

           __a							=    (
               hasattr(__SCREAMING_SNAKE_CASE					,		'''architectures''')
               and isinstance(config.architectures					,		__SCREAMING_SNAKE_CASE)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   __a							=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   __a							=    __import__('''transformers'''					,		fromlist=[model_class])
                   __a							=    getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_cls(__SCREAMING_SNAKE_CASE)
               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:
               __a							=    TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE)

           # encoder-decoder has vocab size saved differently
           __a							=    config.vocab_size if hasattr(__SCREAMING_SNAKE_CASE					,		'''vocab_size''') else config.encoder.vocab_size
           __a							=    random_input_ids(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

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

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

           __a							=    encoder_decoder_train if config.is_encoder_decoder else encoder_train

           return _train



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE					,		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
                   __a							=    timeit.repeat(
                       __SCREAMING_SNAKE_CASE					,		repeat=self.args.repeat					,		number=10					,		)

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





       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Callable[[], None]):


           '''simple docstring'''

           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.''')
                       __a							=    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.''')
                           __a							=    '''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()
                           __a							=    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                           __a							=    nvml.nvmlDeviceGetMemoryInfo(__SCREAMING_SNAKE_CASE)
                           __a							=    meminfo.used
                           __a							=    Memory(__SCREAMING_SNAKE_CASE)
                           # 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.''')
                           __a							=    None
                       else:
                           __a							=    measure_peak_memory_cpu(__SCREAMING_SNAKE_CASE)
                           __a							=    Memory(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                       __a							=    stop_memory_tracing(__SCREAMING_SNAKE_CASE)
                       if memory is None:
                           __a							=    summary.total
                   else:
                       __a							=    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 diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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    _A	(     __UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   str						 =						KandinskyVaaImgaImgPipeline
       UpperCamelCase__  :   Optional[Any]						 =						['''image_embeds''', '''negative_image_embeds''', '''image''']
       UpperCamelCase__  :   Dict						 =						[
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       UpperCamelCase__  :   Any						 =						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase__  :   List[Any]						 =						False



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return self.time_input_dim



       @property
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return self.time_input_dim * 4



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return 100



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)

           __a							=    {
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''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''': '''image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           __a							=    UNetaDConditionModel(**__SCREAMING_SNAKE_CASE)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           return {
               "block_out_channels": [32, 64],
               "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": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    VQModel(**self.dummy_movq_kwargs)
           return model



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.dummy_unet
           __a							=    self.dummy_movq

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.0_00_85,
               '''beta_end''': 0.0_12,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           __a							=    DDIMScheduler(**__SCREAMING_SNAKE_CASE)

           __a							=    {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(seed + 1)).to(
               __SCREAMING_SNAKE_CASE)
           # create init_image
           __a							=    floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    image.cpu().permute(0					,		2					,		3					,		1)[0]
           __a							=    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((256, 256))

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''image''': init_image,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''num_inference_steps''': 10,
               '''guidance_scale''': 7.0,
               '''strength''': 0.2,
               '''output_type''': '''np''',
           }
           return inputs





       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()

           __a							=    self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           __a							=    pipe.to(__SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE))
           __a							=    output.images

           __a							=    pipe(
               **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           __a							=    np.array(
               [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
           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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''')

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

           __a							=    KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''					,		torch_dtype=torch.floataa)
           pipe_prior.to(__SCREAMING_SNAKE_CASE)

           __a							=    KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''					,		torch_dtype=torch.floataa)
           __a							=    pipeline.to(__SCREAMING_SNAKE_CASE)

           pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    torch.Generator(device='''cpu''').manual_seed(0)
           __a	,       __a							=    pipe_prior(
               __SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=5					,		negative_prompt=''''''					,		).to_tuple()

           __a							=    pipeline(
               image=__SCREAMING_SNAKE_CASE					,		image_embeds=__SCREAMING_SNAKE_CASE					,		negative_image_embeds=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=100					,		height=768					,		width=768					,		strength=0.2					,		output_type='''np'''					,		)

           __a							=    output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





