




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	UpperCamelCase__			(A__					,       unittest.TestCase  ):

    '''simple docstring'''



    lowerCamelCase_   :			List[Any]			  =   UnCLIPPipeline
    lowerCamelCase_   :			Union[str, Any]			  =   TEXT_TO_IMAGE_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
        """guidance_scale""",
        """prompt_embeds""",
        """cross_attention_kwargs""",
    }
    lowerCamelCase_   :			Union[str, Any]			  =   TEXT_TO_IMAGE_BATCH_PARAMS
    lowerCamelCase_   :			Any			  =   [
        """generator""",
        """return_dict""",
        """prior_num_inference_steps""",
        """decoder_num_inference_steps""",
        """super_res_num_inference_steps""",
    ]
    lowerCamelCase_   :			Any			  =   False







    @property
    def 					_lowercase  (					self  )  ->   Optional[int]:
           return 32







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return 32







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self.time_input_dim







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           return self.time_input_dim * 4







    @property
    def 					_lowercase  (					self  )  ->   List[str]:
           return 100







    @property
    def 					_lowercase  (					self  )  ->   List[str]:
           lowerCamelCase     :   Any    				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
           return tokenizer







    @property
    def 					_lowercase  (					self  )  ->   int:
           torch.manual_seed(0  )
           lowerCamelCase     :   List[Any]    				=      CLIPTextConfig(
               bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=self.text_embedder_hidden_size  ,						projection_dim=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 CLIPTextModelWithProjection(_lowerCAmelCase  )







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           torch.manual_seed(0  )

           lowerCamelCase     :   Union[str, Any]    				=      {
               "num_attention_heads": 2,
               "attention_head_dim": 12,
               "embedding_dim": self.text_embedder_hidden_size,
               "num_layers": 1,
           }

           lowerCamelCase     :   Dict    				=      PriorTransformer(**_lowerCAmelCase  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           torch.manual_seed(0  )

           lowerCamelCase     :   Union[str, Any]    				=      {
               "clip_embeddings_dim": self.text_embedder_hidden_size,
               "time_embed_dim": self.time_embed_dim,
               "cross_attention_dim": self.cross_attention_dim,
           }

           lowerCamelCase     :   Optional[Any]    				=      UnCLIPTextProjModel(**_lowerCAmelCase  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   int:
           torch.manual_seed(0  )

           lowerCamelCase     :   int    				=      {
               "sample_size": 32,
               # RGB in channels
               "in_channels": 3,
               # Out channels is double in channels because predicts mean and variance
               "out_channels": 6,
               "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,
               "cross_attention_dim": self.cross_attention_dim,
               "attention_head_dim": 4,
               "resnet_time_scale_shift": "scale_shift",
               "class_embed_type": "identity",
           }

           lowerCamelCase     :   Any    				=      UNetaDConditionModel(**_lowerCAmelCase  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           return {
               "sample_size": 64,
               "layers_per_block": 1,
               "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
               "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "in_channels": 6,
               "out_channels": 3,
           }







    @property
    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           torch.manual_seed(0  )

           lowerCamelCase     :   int    				=      UNetaDModel(**self.dummy_super_res_kwargs  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           # seeded differently to get different unet than `self.dummy_super_res_first`
           torch.manual_seed(1  )

           lowerCamelCase     :   Dict    				=      UNetaDModel(**self.dummy_super_res_kwargs  )
           return model







    def 					_lowercase  (					self  )  ->   int:
           lowerCamelCase     :   Tuple    				=      self.dummy_prior
           lowerCamelCase     :   List[Any]    				=      self.dummy_decoder
           lowerCamelCase     :   List[str]    				=      self.dummy_text_proj
           lowerCamelCase     :   Tuple    				=      self.dummy_text_encoder
           lowerCamelCase     :   List[Any]    				=      self.dummy_tokenizer
           lowerCamelCase     :   Any    				=      self.dummy_super_res_first
           lowerCamelCase     :   Optional[int]    				=      self.dummy_super_res_last

           lowerCamelCase     :   Tuple    				=      UnCLIPScheduler(
               variance_type="fixed_small_log"  ,						prediction_type="sample"  ,						num_train_timesteps=1000  ,						clip_sample_range=5.0  ,						)

           lowerCamelCase     :   Union[str, Any]    				=      UnCLIPScheduler(
               variance_type="learned_range"  ,						prediction_type="epsilon"  ,						num_train_timesteps=1000  ,						)

           lowerCamelCase     :   Optional[int]    				=      UnCLIPScheduler(
               variance_type="fixed_small_log"  ,						prediction_type="epsilon"  ,						num_train_timesteps=1000  ,						)

           lowerCamelCase     :   Dict    				=      {
               "prior": prior,
               "decoder": decoder,
               "text_proj": text_proj,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "super_res_first": super_res_first,
               "super_res_last": super_res_last,
               "prior_scheduler": prior_scheduler,
               "decoder_scheduler": decoder_scheduler,
               "super_res_scheduler": super_res_scheduler,
           }

           return components







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=0  )  ->   Dict:
           if str(_lowerCAmelCase  ).startswith("mps"  ):
                  lowerCamelCase     :   Any    				=      torch.manual_seed(_lowerCAmelCase  )
           else:
                  lowerCamelCase     :   List[Any]    				=      torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
           lowerCamelCase     :   List[str]    				=      {
               "prompt": "horse",
               "generator": generator,
               "prior_num_inference_steps": 2,
               "decoder_num_inference_steps": 2,
               "super_res_num_inference_steps": 2,
               "output_type": "numpy",
           }
           return inputs







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           lowerCamelCase     :   Any    				=      "cpu"

           lowerCamelCase     :   Union[str, Any]    				=      self.get_dummy_components()

           lowerCamelCase     :   List[str]    				=      self.pipeline_class(**_lowerCAmelCase  )
           lowerCamelCase     :   int    				=      pipe.to(_lowerCAmelCase  )

           pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

           lowerCamelCase     :   int    				=      pipe(**self.get_dummy_inputs(_lowerCAmelCase  )  )
           lowerCamelCase     :   Optional[Any]    				=      output.images

           lowerCamelCase     :   Tuple    				=      pipe(
               **self.get_dummy_inputs(_lowerCAmelCase  )  ,						return_dict=_lowerCAmelCase  ,						)[0]

           lowerCamelCase     :   List[Any]    				=      image[0, -3:, -3:, -1]
           lowerCamelCase     :   List[str]    				=      image_from_tuple[0, -3:, -3:, -1]

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

           lowerCamelCase     :   Tuple    				=      np.array(
               [
                   0.9997,
                   0.9988,
                   0.0028,
                   0.9997,
                   0.9984,
                   0.9965,
                   0.0029,
                   0.9986,
                   0.0025,
               ]  )

           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 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   str    				=      torch.device("cpu"  )




           class 	UpperCamelCase__			:

               '''simple docstring'''



               lowerCamelCase_   :			List[str]			  =   1




           lowerCamelCase     :   str    				=      self.get_dummy_components()

           lowerCamelCase     :   Optional[int]    				=      self.pipeline_class(**_lowerCAmelCase  )
           lowerCamelCase     :   Any    				=      pipe.to(_lowerCAmelCase  )

           lowerCamelCase     :   int    				=      components["prior"]
           lowerCamelCase     :   int    				=      components["decoder"]
           lowerCamelCase     :   Optional[Any]    				=      components["super_res_first"]
           lowerCamelCase     :   Optional[int]    				=      components["tokenizer"]
           lowerCamelCase     :   Union[str, Any]    				=      components["text_encoder"]

           lowerCamelCase     :   Dict    				=      torch.Generator(device=_lowerCAmelCase  ).manual_seed(0  )
           lowerCamelCase     :   str    				=      prior.dtype
           lowerCamelCase     :   Union[str, Any]    				=      1

           lowerCamelCase     :   int    				=      (batch_size, prior.config.embedding_dim)
           lowerCamelCase     :   Optional[int]    				=      pipe.prepare_latents(
               _lowerCAmelCase  ,						dtype=_lowerCAmelCase  ,						device=_lowerCAmelCase  ,						generator=_lowerCAmelCase  ,						latents=_lowerCAmelCase  ,						scheduler=DummyScheduler()  )
           lowerCamelCase     :   Union[str, Any]    				=      (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
           lowerCamelCase     :   Dict    				=      pipe.prepare_latents(
               _lowerCAmelCase  ,						dtype=_lowerCAmelCase  ,						device=_lowerCAmelCase  ,						generator=_lowerCAmelCase  ,						latents=_lowerCAmelCase  ,						scheduler=DummyScheduler()  )

           lowerCamelCase     :   Tuple    				=      (
               batch_size,
               super_res_first.config.in_channels // 2,
               super_res_first.config.sample_size,
               super_res_first.config.sample_size,
           )
           lowerCamelCase     :   Any    				=      pipe.prepare_latents(
               _lowerCAmelCase  ,						dtype=_lowerCAmelCase  ,						device=_lowerCAmelCase  ,						generator=_lowerCAmelCase  ,						latents=_lowerCAmelCase  ,						scheduler=DummyScheduler()  )

           pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

           lowerCamelCase     :   str    				=      "this is a prompt example"

           lowerCamelCase     :   Optional[Any]    				=      torch.Generator(device=_lowerCAmelCase  ).manual_seed(0  )
           lowerCamelCase     :   int    				=      pipe(
               [prompt]  ,						generator=_lowerCAmelCase  ,						prior_num_inference_steps=2  ,						decoder_num_inference_steps=2  ,						super_res_num_inference_steps=2  ,						prior_latents=_lowerCAmelCase  ,						decoder_latents=_lowerCAmelCase  ,						super_res_latents=_lowerCAmelCase  ,						output_type="np"  ,						)
           lowerCamelCase     :   Any    				=      output.images

           lowerCamelCase     :   int    				=      tokenizer(
               _lowerCAmelCase  ,						padding="max_length"  ,						max_length=tokenizer.model_max_length  ,						return_tensors="pt"  ,						)
           lowerCamelCase     :   Tuple    				=      text_encoder(text_inputs.input_ids  )
           lowerCamelCase     :   List[Any]    				=      text_inputs.attention_mask

           lowerCamelCase     :   Dict    				=      torch.Generator(device=_lowerCAmelCase  ).manual_seed(0  )
           lowerCamelCase     :   str    				=      pipe(
               generator=_lowerCAmelCase  ,						prior_num_inference_steps=2  ,						decoder_num_inference_steps=2  ,						super_res_num_inference_steps=2  ,						prior_latents=_lowerCAmelCase  ,						decoder_latents=_lowerCAmelCase  ,						super_res_latents=_lowerCAmelCase  ,						text_model_output=_lowerCAmelCase  ,						text_attention_mask=_lowerCAmelCase  ,						output_type="np"  ,						)[0]

           # make sure passing text embeddings manually is identical
           assert np.abs(image - image_from_text  ).max() < 1e-4







    @skip_mps
    def 					_lowercase  (					self  )  ->   int:
           lowerCamelCase     :   Optional[Any]    				=      torch_device == "cpu"

           self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase  ,						expected_max_diff=0.01  )







    @skip_mps
    def 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   Any    				=      torch_device == "cpu"
           lowerCamelCase     :   Optional[Any]    				=      True
           lowerCamelCase     :   Optional[int]    				=      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           self._test_inference_batch_single_identical(
               test_max_difference=_lowerCAmelCase  ,						relax_max_difference=_lowerCAmelCase  ,						additional_params_copy_to_batched_inputs=_lowerCAmelCase  ,						)







    def 					_lowercase  (					self  )  ->   str:
           lowerCamelCase     :   Union[str, Any]    				=      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           if torch_device == "mps":
                  # TODO: MPS errors with larger batch sizes
                  lowerCamelCase     :   Dict    				=      [2, 3]
                  self._test_inference_batch_consistent(
                      batch_sizes=_lowerCAmelCase  ,						additional_params_copy_to_batched_inputs=_lowerCAmelCase  ,						)
           else:
                  self._test_inference_batch_consistent(
                      additional_params_copy_to_batched_inputs=_lowerCAmelCase  )







    @skip_mps
    def 					_lowercase  (					self  )  ->   Optional[int]:
           return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def 					_lowercase  (					self  )  ->   str:
           return super().test_save_load_local()







    @skip_mps
    def 					_lowercase  (					self  )  ->   Tuple:
           return super().test_save_load_optional_components()




@nightly
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           lowerCamelCase     :   Optional[int]    				=      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_cpu.npy"  )

           lowerCamelCase     :   Tuple    				=      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  )
           pipeline.set_progress_bar_config(disable=_lowerCAmelCase  )

           lowerCamelCase     :   Union[str, Any]    				=      torch.manual_seed(0  )
           lowerCamelCase     :   Optional[int]    				=      pipeline(
               "horse"  ,						num_images_per_prompt=1  ,						generator=_lowerCAmelCase  ,						output_type="np"  ,						)

           lowerCamelCase     :   Optional[Any]    				=      output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image  ).max() < 1e-1




@slow
@require_torch_gpu
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   Optional[Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   List[Any]    				=      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_fp16.npy"  )

           lowerCamelCase     :   Optional[int]    				=      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,						torch_dtype=torch.floataa  )
           lowerCamelCase     :   str    				=      pipeline.to(_lowerCAmelCase  )
           pipeline.set_progress_bar_config(disable=_lowerCAmelCase  )

           lowerCamelCase     :   Any    				=      torch.Generator(device="cpu"  ).manual_seed(0  )
           lowerCamelCase     :   Optional[int]    				=      pipeline(
               "horse"  ,						generator=_lowerCAmelCase  ,						output_type="np"  ,						)

           lowerCamelCase     :   Dict    				=      output.images[0]

           assert image.shape == (256, 256, 3)

           assert_mean_pixel_difference(_lowerCAmelCase  ,						_lowerCAmelCase  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           lowerCamelCase     :   Dict    				=      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,						torch_dtype=torch.floataa  )
           lowerCamelCase     :   Optional[int]    				=      pipe.to(_lowerCAmelCase  )
           pipe.set_progress_bar_config(disable=_lowerCAmelCase  )
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           lowerCamelCase     :   Dict    				=      pipe(
               "horse"  ,						num_images_per_prompt=1  ,						prior_num_inference_steps=2  ,						decoder_num_inference_steps=2  ,						super_res_num_inference_steps=2  ,						output_type="np"  ,						)

           lowerCamelCase     :   Dict    				=      torch.cuda.max_memory_allocated()
           # make sure that less than 7 GB is allocated
           assert mem_bytes < 7 * 10**9











import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
       import torch

       from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
       import pyanvml.pyanvml as nvml


SCREAMING_SNAKE_CASE__ :   List[Any]						=     logging.get_logger(__name__)




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			PyTorchBenchmarkArguments
    lowerCamelCase_   :			PretrainedConfig
    lowerCamelCase_   :			str			  =   "PyTorch"







    @property
    def 					_lowercase  (					self  )  ->   Optional[int]:
           return torch.__version__







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   float:
           lowerCamelCase     :   List[Any]    				=      self._prepare_inference_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_speed(_inference  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   [Memory, Optional[MemorySummary]]:
           lowerCamelCase     :   List[Any]    				=      self._prepare_inference_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_memory(_inference  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   float:
           lowerCamelCase     :   Dict    				=      self._prepare_train_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_speed(_train  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   [Memory, Optional[MemorySummary]]:
           lowerCamelCase     :   Optional[Any]    				=      self._prepare_train_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_memory(_train  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Callable[[], None]:
           lowerCamelCase     :   Optional[Any]    				=      self.config_dict[model_name]

           if self.args.torchscript:
                  lowerCamelCase     :   Optional[Any]    				=      True

           lowerCamelCase     :   Dict    				=      (
               hasattr(UpperCamelCase__  ,						"architectures"  )
               and isinstance(config.architectures  ,						UpperCamelCase__  )
               and len(config.architectures  ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                  try:
                         lowerCamelCase     :   Optional[Any]    				=      config.architectures[0]
                         lowerCamelCase     :   Optional[int]    				=      __import__("transformers"  ,						fromlist=[model_class]  )
                         lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      model_cls(UpperCamelCase__  )
                  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:
                  lowerCamelCase     :   Tuple    				=      MODEL_MAPPING[config.__class__](UpperCamelCase__  )

           model.eval()
           model.to(self.args.device  )

           # encoder-decoder has vocab size saved differently
           lowerCamelCase     :   str    				=      config.vocab_size if hasattr(UpperCamelCase__  ,						"vocab_size"  ) else config.encoder.vocab_size
           lowerCamelCase     :   int    				=      torch.randint(UpperCamelCase__  ,						(batch_size, sequence_length)  ,						dtype=torch.long  ,						device=self.args.device  )

           if self.args.fpaa:
                  logger.info("Running training in Mixed Precision..."  )
                  if not self.args.is_gpu:
                         raise ValueError("Mixed precision is possible only for GPU."  )
                  # amp seems to have memory leaks so that memory usage
                  # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                  model.half()

           if self.args.torchscript:
                  with torch.no_grad():
                         lowerCamelCase     :   Optional[int]    				=      torch.jit.trace(UpperCamelCase__  ,						UpperCamelCase__  )
           else:
                  lowerCamelCase     :   Dict    				=      model

           def encoder_decoder_forward():
                  with torch.no_grad():
                         lowerCamelCase     :   Dict    				=      inference_model(UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  )
                  return outputs

           def encoder_forward():
                  with torch.no_grad():
                         lowerCamelCase     :   Union[str, Any]    				=      inference_model(UpperCamelCase__  )
                  return outputs

           lowerCamelCase     :   Dict    				=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
           return _forward







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Callable[[], None]:
           lowerCamelCase     :   Optional[int]    				=      self.config_dict[model_name]

           lowerCamelCase     :   List[Any]    				=      (
               hasattr(UpperCamelCase__  ,						"architectures"  )
               and isinstance(config.architectures  ,						UpperCamelCase__  )
               and len(config.architectures  ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                  try:
                         lowerCamelCase     :   Union[str, Any]    				=      config.architectures[0]
                         lowerCamelCase     :   int    				=      __import__("transformers"  ,						fromlist=[model_class]  )
                         lowerCamelCase     :   Tuple    				=      getattr(UpperCamelCase__  ,						UpperCamelCase__  )
                         lowerCamelCase     :   Tuple    				=      model_cls(UpperCamelCase__  )
                  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:
                  lowerCamelCase     :   List[str]    				=      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__  )

           if self.args.torchscript:
                  raise NotImplementedError("Training for torchscript is currently not implemented"  )
           else:
                  lowerCamelCase     :   Dict    				=      model

           model.train()
           model.to(self.args.device  )

           # encoder-decoder has vocab size saved differently
           lowerCamelCase     :   Union[str, Any]    				=      config.vocab_size if hasattr(UpperCamelCase__  ,						"vocab_size"  ) else config.encoder.vocab_size
           lowerCamelCase     :   List[str]    				=      torch.randint(UpperCamelCase__  ,						(batch_size, sequence_length)  ,						dtype=torch.long  ,						device=self.args.device  )

           if self.args.fpaa:
                  logger.info("Running training in Mixed Precision..."  )
                  if not self.args.is_gpu:
                         raise ValueError("Mixed precision is possible only for GPU."  )

                  # amp seems to have memory leaks so that memory usage
                  # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                  model.half()

           def compute_loss_and_backprob_encoder():
                  lowerCamelCase     :   Tuple    				=      train_model(UpperCamelCase__  ,						labels=UpperCamelCase__  )[0]
                  loss.backward()
                  return loss

           def compute_loss_and_backprob_encoder_decoder():
                  lowerCamelCase     :   Optional[int]    				=      train_model(UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  )[0]
                  loss.backward()
                  return loss

           lowerCamelCase     :   Union[str, Any]    				=      (
               compute_loss_and_backprob_encoder_decoder
               if config.is_encoder_decoder
               else compute_loss_and_backprob_encoder
           )
           return _train







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   float:
           try:
                  if self.args.is_tpu or self.args.torchscript:
                         # run additional 10 times to stabilize compilation for tpu and torchscript
                         logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"  )
                         timeit.repeat(
                             UpperCamelCase__  ,						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
                  lowerCamelCase     :   List[str]    				=      timeit.repeat(
                      UpperCamelCase__  ,						repeat=self.args.repeat  ,						number=10  ,						)

                  if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                         import torch_xla.debug.metrics as met

                         self.print_fn(met.metrics_report()  )

                  return min(UpperCamelCase__  ) / 10.0
           except RuntimeError as e:
                  self.print_fn(F'''Doesn\'t fit on GPU. {e}'''  )
                  return "N/A"







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   [Memory, MemorySummary]:
           try:
                  if self.args.trace_memory_line_by_line:
                         lowerCamelCase     :   str    				=      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"
                             " `--no-memory` or `args.memory=False`"  )
                  elif self.args.is_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."  )
                                lowerCamelCase     :   Optional[Any]    				=      "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()
                                lowerCamelCase     :   int    				=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
                                lowerCamelCase     :   Dict    				=      nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__  )
                                lowerCamelCase     :   int    				=      meminfo.used
                                lowerCamelCase     :   Dict    				=      Memory(UpperCamelCase__  )
                                # shutdown nvml
                                nvml.nvmlShutdown()
                  else:
                         # cpu
                         lowerCamelCase     :   Union[str, Any]    				=      measure_peak_memory_cpu(UpperCamelCase__  )
                         lowerCamelCase     :   int    				=      Memory(UpperCamelCase__  ) if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ) else memory_bytes

                  if self.args.trace_memory_line_by_line:
                         lowerCamelCase     :   List[str]    				=      stop_memory_tracing(UpperCamelCase__  )
                  else:
                         lowerCamelCase     :   int    				=      None

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






