"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[str]     							=							StableDiffusionControlNetImgaImgPipeline
    _snake_case     :  Optional[int]     							=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    _snake_case     :  Optional[int]     							=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _snake_case     :  List[str]     							=							IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}   )
    _snake_case     :  Union[str, Any]     							=							IMAGE_TO_IMAGE_IMAGE_PARAMS


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      torch.manual_seed(0					)
      _UpperCAmelCase											=      UNetaDConditionModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,				cross_attention_dim=32   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      ControlNetModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      DDIMScheduler(
          beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=__lowerCAmelCase   ,				set_alpha_to_one=__lowerCAmelCase   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      AutoencoderKL(
          block_out_channels=[32, 64]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,				latent_channels=4   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      CLIPTextConfig(
          bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
      _UpperCAmelCase											=      CLIPTextModel(__lowerCAmelCase					)
      _UpperCAmelCase											=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      _UpperCAmelCase											=      {
          """unet""": unet,
          """controlnet""": controlnet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase											=      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase											=      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)

      _UpperCAmelCase											=      2
      _UpperCAmelCase											=      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__lowerCAmelCase   ,				device=torch.device(__lowerCAmelCase					)   ,				)
      _UpperCAmelCase											=      floats_tensor(control_image.shape   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase											=      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase											=      Image.fromarray(np.uinta(__lowerCAmelCase					)					).convert("""RGB"""					).resize((64, 64)					)
      _UpperCAmelCase											=      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
          """image""": image,
          """control_image""": control_image,
      }

      return inputs


    def      lowerCAmelCase_  (					self	:  Dict					):
      return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)


    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
    def      lowerCAmelCase_  (					self	:  Tuple					):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      self._test_inference_batch_single_identical(expected_max_diff=2e-3					)



class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[Any]     							=							StableDiffusionControlNetImgaImgPipeline
    _snake_case     :  int     							=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    _snake_case     :  Any     							=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _snake_case     :  Optional[int]     							=							frozenset([]   )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


    def      lowerCAmelCase_  (					self	:  Any					):
      torch.manual_seed(0					)
      _UpperCAmelCase											=      UNetaDConditionModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,				cross_attention_dim=32   ,				)
      torch.manual_seed(0					)

      def init_weights(__lowerCAmelCase	:  str					):
        if isinstance(__lowerCAmelCase   ,				torch.nn.Convad					):
          torch.nn.init.normal(m.weight					)
          m.bias.data.fill_(1.0					)

      _UpperCAmelCase											=      ControlNetModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
      controlneta.controlnet_down_blocks.apply(__lowerCAmelCase					)

      torch.manual_seed(0					)
      _UpperCAmelCase											=      ControlNetModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
      controlneta.controlnet_down_blocks.apply(__lowerCAmelCase					)

      torch.manual_seed(0					)
      _UpperCAmelCase											=      DDIMScheduler(
          beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=__lowerCAmelCase   ,				set_alpha_to_one=__lowerCAmelCase   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      AutoencoderKL(
          block_out_channels=[32, 64]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,				latent_channels=4   ,				)
      torch.manual_seed(0					)
      _UpperCAmelCase											=      CLIPTextConfig(
          bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
      _UpperCAmelCase											=      CLIPTextModel(__lowerCAmelCase					)
      _UpperCAmelCase											=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      _UpperCAmelCase											=      MultiControlNetModel([controlneta, controlneta]					)

      _UpperCAmelCase											=      {
          """unet""": unet,
          """controlnet""": controlnet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[str]=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase											=      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase											=      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)

      _UpperCAmelCase											=      2

      _UpperCAmelCase											=      [
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__lowerCAmelCase   ,				device=torch.device(__lowerCAmelCase					)   ,				),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__lowerCAmelCase   ,				device=torch.device(__lowerCAmelCase					)   ,				),
      ]

      _UpperCAmelCase											=      floats_tensor(control_image[0].shape   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase											=      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase											=      Image.fromarray(np.uinta(__lowerCAmelCase					)					).convert("""RGB"""					).resize((64, 64)					)
      _UpperCAmelCase											=      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
          """image""": image,
          """control_image""": control_image,
      }

      return inputs


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase											=      self.get_dummy_components()
      _UpperCAmelCase											=      self.pipeline_class(**__lowerCAmelCase					)
      pipe.to(__lowerCAmelCase					)

      _UpperCAmelCase											=      10.0
      _UpperCAmelCase											=      4

      _UpperCAmelCase											=      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase											=      steps
      _UpperCAmelCase											=      scale
      _UpperCAmelCase											=      pipe(**__lowerCAmelCase					)[0]

      _UpperCAmelCase											=      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase											=      steps
      _UpperCAmelCase											=      scale
      _UpperCAmelCase											=      pipe(**__lowerCAmelCase   ,				control_guidance_start=0.1   ,				control_guidance_end=0.2					)[0]

      _UpperCAmelCase											=      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase											=      steps
      _UpperCAmelCase											=      scale
      _UpperCAmelCase											=      pipe(**__lowerCAmelCase   ,				control_guidance_start=[0.1, 0.3]   ,				control_guidance_end=[0.2, 0.7]					)[0]

      _UpperCAmelCase											=      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase											=      steps
      _UpperCAmelCase											=      scale
      _UpperCAmelCase											=      pipe(**__lowerCAmelCase   ,				control_guidance_start=0.4   ,				control_guidance_end=[0.5, 0.8]					)[0]

      # make sure that all outputs are different
      assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
      assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
      assert np.sum(np.abs(output_a - output_a					)					) > 1e-3


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)


    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
    def      lowerCAmelCase_  (					self	:  Dict					):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      self._test_inference_batch_single_identical(expected_max_diff=2e-3					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase											=      self.get_dummy_components()
      _UpperCAmelCase											=      self.pipeline_class(**__lowerCAmelCase					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      with tempfile.TemporaryDirectory() as tmpdir:
        try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(__lowerCAmelCase					)
        except NotImplementedError:
          pass



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


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase											=      ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""					)

      _UpperCAmelCase											=      StableDiffusionControlNetImgaImgPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""   ,				safety_checker=__lowerCAmelCase   ,				controlnet=__lowerCAmelCase					)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

      _UpperCAmelCase											=      torch.Generator(device="""cpu"""					).manual_seed(0					)
      _UpperCAmelCase											=      """evil space-punk bird"""
      _UpperCAmelCase											=      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""					).resize((512, 512)					)
      _UpperCAmelCase											=      load_image(
          """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""					).resize((512, 512)					)

      _UpperCAmelCase											=      pipe(
          __lowerCAmelCase   ,				__lowerCAmelCase   ,				control_image=__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				output_type="""np"""   ,				num_inference_steps=50   ,				strength=0.6   ,				)

      _UpperCAmelCase											=      output.images[0]

      assert image.shape == (512, 512, 3)

      _UpperCAmelCase											=      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""					)

      assert np.abs(expected_image - image					).max() < 9e-2

"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase__  		=						logging.getLogger(__name__)


@dataclass
class        a				:
    _snake_case     :  str     							=							field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
    _snake_case     :  Optional[str]     							=							field(
        default=lowerCAmelCase_	,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
    _snake_case     :  Optional[str]     							=							field(
        default=lowerCAmelCase_	,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
    _snake_case     :  Optional[str]     							=							field(
        default=lowerCAmelCase_	,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,							)
    _snake_case     :  bool     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Whether tp freeze the encoder.'}   )
    _snake_case     :  bool     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Whether  to freeze the embeddings.'}   )



@dataclass
class        a				:
    _snake_case     :  str     							=							field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
    _snake_case     :  Optional[str]     							=							field(
        default='summarization'	,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=10_24	,							metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_28	,							metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_42	,							metadata={
            'help': (
                'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded. '
                'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                'during ``evaluate`` and ``predict``.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_42	,							metadata={
            'help': (
                'The maximum total sequence length for test target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# training examples. -1 means use all.'}   )
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# validation examples. -1 means use all.'}   )
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# test examples. -1 means use all.'}   )
    _snake_case     :  Optional[str]     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Source language id for translation.'}   )
    _snake_case     :  Optional[str]     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Target language id for translation.'}   )
    _snake_case     :  Optional[int]     							=							field(default=lowerCAmelCase_	,							metadata={'help': '# num_beams to use for evaluation.'}   )
    _snake_case     :  bool     							=							field(
        default=lowerCAmelCase_	,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,							)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  logger.info(f'''***** {split} metrics *****'''		)
  for key in sorted(metrics.keys()		):
    logger.info(f'''  {key} = {metrics[key]}'''		)
  save_json(lowercase   ,os.path.join(lowercase   ,f'''{split}_results.json'''		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  _UpperCAmelCase						 =      HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)		)

  if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      parser.parse_args_into_dataclasses()

  check_output_dir(lowercase		)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,datefmt="""%m/%d/%Y %H:%M:%S"""   ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,)
  logger.warning(
      """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""   ,training_args.local_rank   ,training_args.device   ,training_args.n_gpu   ,bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED		)   ,training_args.fpaa   ,)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank		):
    transformers.utils.logging.set_verbosity_info()
  logger.info("""Training/evaluation parameters %s"""   ,lowercase		)

  # Set seed
  set_seed(training_args.seed		)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _UpperCAmelCase						 =      AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)

  _UpperCAmelCase						 =      ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  for p in extra_model_params:
    if getattr(lowercase   ,lowercase   ,lowercase		):
      assert hasattr(lowercase   ,lowercase		), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
      setattr(lowercase   ,lowercase   ,getattr(lowercase   ,lowercase		)		)

  _UpperCAmelCase						 =      AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)
  _UpperCAmelCase						 =      AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path   ,from_tf=""".ckpt""" in model_args.model_name_or_path   ,config=lowercase   ,cache_dir=model_args.cache_dir   ,)

  # use task specific params
  use_task_specific_params(lowercase   ,data_args.task		)

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    _UpperCAmelCase						 =      model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(lowercase   ,(MBartTokenizer, MBartTokenizerFast)		):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(lowercase   ,lowercase		):
      _UpperCAmelCase						 =      tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      _UpperCAmelCase						 =      tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

  if model_args.freeze_embeds:
    freeze_embeds(lowercase		)
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()		)
    assert_all_frozen(model.get_encoder()		)

  _UpperCAmelCase						 =      SeqaSeqDataset

  # Get datasets
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,type_path="""train"""   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_train   ,max_target_length=data_args.max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or """"""   ,)
      if training_args.do_train
      else None
  )
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,type_path="""val"""   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_val   ,max_target_length=data_args.val_max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or """"""   ,)
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,type_path="""test"""   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_test   ,max_target_length=data_args.test_max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or """"""   ,)
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  _UpperCAmelCase						 =      (
      build_compute_metrics_fn(data_args.task   ,lowercase		) if training_args.predict_with_generate else None
  )
  _UpperCAmelCase						 =      SeqaSeqTrainer(
      model=lowercase   ,args=lowercase   ,data_args=lowercase   ,train_dataset=lowercase   ,eval_dataset=lowercase   ,data_collator=SeqaSeqDataCollator(
          lowercase   ,lowercase   ,model.config.decoder_start_token_id   ,training_args.tpu_num_cores		)   ,compute_metrics=lowercase   ,tokenizer=lowercase   ,)

  _UpperCAmelCase						 =      {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***"""		)

    _UpperCAmelCase						 =      trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
    _UpperCAmelCase						 =      train_result.metrics
    _UpperCAmelCase						 =      data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics("""train"""   ,lowercase   ,training_args.output_dir		)
      all_metrics.update(lowercase		)

      # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir   ,"""trainer_state.json"""		)		)

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""		)

    _UpperCAmelCase						 =      trainer.evaluate(metric_key_prefix="""val"""		)
    _UpperCAmelCase						 =      data_args.n_val
    _UpperCAmelCase						 =      round(metrics["""val_loss"""]   ,4		)

    if trainer.is_world_process_zero():
      handle_metrics("""val"""   ,lowercase   ,training_args.output_dir		)
      all_metrics.update(lowercase		)

  if training_args.do_predict:
    logger.info("""*** Predict ***"""		)

    _UpperCAmelCase						 =      trainer.predict(test_dataset=lowercase   ,metric_key_prefix="""test"""		)
    _UpperCAmelCase						 =      test_output.metrics
    _UpperCAmelCase						 =      data_args.n_test

    if trainer.is_world_process_zero():
      _UpperCAmelCase						 =      round(metrics["""test_loss"""]   ,4		)
      handle_metrics("""test"""   ,lowercase   ,training_args.output_dir		)
      all_metrics.update(lowercase		)

      if training_args.predict_with_generate:
        _UpperCAmelCase						 =      tokenizer.batch_decode(
            test_output.predictions   ,skip_special_tokens=lowercase   ,clean_up_tokenization_spaces=lowercase		)
        _UpperCAmelCase						 =      lmap(str.strip   ,lowercase		)
        write_txt_file(lowercase   ,os.path.join(training_args.output_dir   ,"""test_generations.txt"""		)		)

  if trainer.is_world_process_zero():
    save_json(lowercase   ,os.path.join(training_args.output_dir   ,"""all_results.json"""		)		)

  return all_metrics






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()
