
"""simple docstring"""






import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase       =  logging.get_logger(__name__)

set_seed(7_70)


__UpperCAmelCase       =  {
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


__UpperCAmelCase       =  {
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

__UpperCAmelCase       =  os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase       =  os.path.join(os.path.expanduser('~'), '.cache')
__UpperCAmelCase       =  os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')






def 						_snake_case       (	lowercase__		:  List[str]      ,     lowercase__		:  Union[str, Any]=False		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Any			     =  model_type
			if use_small:
						key += "_small"
			return os.path.join(lowercase__      ,     REMOTE_MODEL_PATHS[key]["""file_name"""]		)






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Optional[Any]		)  ->				Tuple:


			'''simple docstring'''





			os.makedirs(lowercase__      ,     exist_ok=lowercase__		)
			hf_hub_download(repo_id=lowercase__      ,     filename=lowercase__      ,     local_dir=lowercase__		)






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  Optional[Any]=False      ,     lowercase__		:  Optional[Any]="text"		)  ->				List[str]:


			'''simple docstring'''





			if model_type == "text":
						lowerCAmelCase_    :List[Any]			     =  BarkSemanticModel
						lowerCAmelCase_    :int			     =  BarkSemanticConfig
						lowerCAmelCase_    :Optional[Any]			     =  BarkSemanticGenerationConfig
			elif model_type == "coarse":
						lowerCAmelCase_    :Any			     =  BarkCoarseModel
						lowerCAmelCase_    :List[Any]			     =  BarkCoarseConfig
						lowerCAmelCase_    :Dict			     =  BarkCoarseGenerationConfig
			elif model_type == "fine":
						lowerCAmelCase_    :List[str]			     =  BarkFineModel
						lowerCAmelCase_    :int			     =  BarkFineConfig
						lowerCAmelCase_    :Optional[Any]			     =  BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			lowerCAmelCase_    :Tuple			     =  f"""{model_type}_small""" if use_small else model_type
			lowerCAmelCase_    :Tuple			     =  REMOTE_MODEL_PATHS[model_key]
			if not os.path.exists(lowercase__		):
						logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""		)
						_download(model_info["""repo_id"""]      ,     model_info["""file_name"""]		)
			lowerCAmelCase_    :Optional[Any]			     =  torch.load(lowercase__      ,     map_location=lowercase__		)
			# this is a hack
			lowerCAmelCase_    :Tuple			     =  checkpoint["""model_args"""]
			if "input_vocab_size" not in model_args:
						lowerCAmelCase_    :Tuple			     =  model_args["""vocab_size"""]
						lowerCAmelCase_    :Dict			     =  model_args["""vocab_size"""]
						del model_args["vocab_size"]

			# convert Bark model arguments to HF Bark model arguments
			lowerCAmelCase_    :str			     =  model_args.pop("""n_head"""		)
			lowerCAmelCase_    :Any			     =  model_args.pop("""n_embd"""		)
			lowerCAmelCase_    :Optional[int]			     =  model_args.pop("""n_layer"""		)

			lowerCAmelCase_    :Optional[Any]			     =  ConfigClass(**checkpoint["""model_args"""]		)
			lowerCAmelCase_    :Union[str, Any]			     =  ModelClass(config=lowercase__		)
			lowerCAmelCase_    :List[str]			     =  GenerationConfigClass()

			lowerCAmelCase_    :int			     =  model_generation_config
			lowerCAmelCase_    :Optional[int]			     =  checkpoint["""model"""]
			# fixup checkpoint
			lowerCAmelCase_    :Union[str, Any]			     =  """_orig_mod."""
			for k, v in list(state_dict.items()		):
						if k.startswith(lowercase__		):
									# replace part of the key with corresponding layer name in HF implementation
									lowerCAmelCase_    :List[Any]			     =  k[len(lowercase__		) :]
									for old_layer_name in new_layer_name_dict:
												lowerCAmelCase_    :Any			     =  new_k.replace(lowercase__      ,     new_layer_name_dict[old_layer_name]		)

									lowerCAmelCase_    :int			     =  state_dict.pop(lowercase__		)

			lowerCAmelCase_    :int			     =  set(state_dict.keys()		) - set(model.state_dict().keys()		)
			lowerCAmelCase_    :Union[str, Any]			     =  {k for k in extra_keys if not k.endswith(""".attn.bias"""		)}
			lowerCAmelCase_    :Union[str, Any]			     =  set(model.state_dict().keys()		) - set(state_dict.keys()		)
			lowerCAmelCase_    :List[Any]			     =  {k for k in missing_keys if not k.endswith(""".attn.bias"""		)}
			if len(lowercase__		) != 0:
						raise ValueError(f"""extra keys found: {extra_keys}"""		)
			if len(lowercase__		) != 0:
						raise ValueError(f"""missing keys: {missing_keys}"""		)
			model.load_state_dict(lowercase__      ,     strict=lowercase__		)
			lowerCAmelCase_    :List[Any]			     =  model.num_parameters(exclude_embeddings=lowercase__		)
			lowerCAmelCase_    :List[str]			     =  checkpoint["""best_val_loss"""].item()
			logger.info(f"""model loaded: {round(n_params/1E6      ,     1		)}M params, {round(lowercase__      ,     3		)} loss"""		)
			model.eval()
			model.to(lowercase__		)
			del checkpoint, state_dict

			return model






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  List[str]=False      ,     lowercase__		:  Dict="text"		)  ->				Any:


			'''simple docstring'''





			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			lowerCAmelCase_    :List[str]			     =  """cpu"""  # do conversion on cpu

			lowerCAmelCase_    :Tuple			     =  _get_ckpt_path(lowercase__      ,     use_small=lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  _load_model(lowercase__      ,     lowercase__      ,     model_type=lowercase__      ,     use_small=lowercase__		)

			# load bark initial model
			lowerCAmelCase_    :Optional[int]			     =  _bark_load_model(lowercase__      ,     """cpu"""      ,     model_type=lowercase__      ,     use_small=lowercase__		)

			if model_type == "text":
						lowerCAmelCase_    :Tuple			     =  bark_model["""model"""]

			if model.num_parameters(exclude_embeddings=lowercase__		) != bark_model.get_num_params():
						raise ValueError("""initial and new models don\'t have the same number of parameters"""		)

			# check if same output as the bark model
			lowerCAmelCase_    :Optional[int]			     =  5
			lowerCAmelCase_    :List[str]			     =  1_0

			if model_type in ["text", "coarse"]:
						lowerCAmelCase_    :int			     =  torch.randint(2_5_6      ,     (batch_size, sequence_length)      ,     dtype=torch.int		)
						lowerCAmelCase_    :Tuple			     =  bark_model(lowercase__		)[0]

						lowerCAmelCase_    :int			     =  model(lowercase__		)

						# take last logits
						lowerCAmelCase_    :Any			     =  output_new_model_total.logits[:, [-1], :]

			else:
						lowerCAmelCase_    :int			     =  3
						lowerCAmelCase_    :Any			     =  8
						lowerCAmelCase_    :Optional[Any]			     =  torch.randint(2_5_6      ,     (batch_size, sequence_length, n_codes_total)      ,     dtype=torch.int		)

						lowerCAmelCase_    :int			     =  model(lowercase__      ,     lowercase__		)
						lowerCAmelCase_    :Optional[Any]			     =  bark_model(lowercase__      ,     lowercase__		)

						lowerCAmelCase_    :Dict			     =  output_new_model_total.logits

			# output difference should come from the difference of self-attention implementation design
			if output_new_model.shape != output_old_model.shape:
						raise ValueError("""initial and new outputs don\'t have the same shape"""		)
			if (output_new_model - output_old_model).abs().max().item() > 1E-3:
						raise ValueError("""initial and new outputs are not equal"""		)

			Path(lowercase__		).mkdir(exist_ok=lowercase__		)
			model.save_pretrained(lowercase__		)






def 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  Dict      ,     lowercase__		:  List[str]      ,     lowercase__		:  Tuple      ,     lowercase__		:  Optional[int]      ,     )  ->				Union[str, Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  os.path.join(lowercase__      ,     lowercase__		)

			lowerCAmelCase_    :Tuple			     =  BarkSemanticConfig.from_pretrained(os.path.join(lowercase__      ,     """config.json"""		)		)
			lowerCAmelCase_    :List[Any]			     =  BarkCoarseConfig.from_pretrained(os.path.join(lowercase__      ,     """config.json"""		)		)
			lowerCAmelCase_    :str			     =  BarkFineConfig.from_pretrained(os.path.join(lowercase__      ,     """config.json"""		)		)
			lowerCAmelCase_    :Tuple			     =  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""		)

			lowerCAmelCase_    :Optional[Any]			     =  BarkSemanticModel.from_pretrained(lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  BarkCoarseModel.from_pretrained(lowercase__		)
			lowerCAmelCase_    :Any			     =  BarkFineModel.from_pretrained(lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""		)

			lowerCAmelCase_    :Optional[Any]			     =  BarkConfig.from_sub_model_configs(
			    lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)

			lowerCAmelCase_    :List[Any]			     =  BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config      ,     coarseAcoustic.generation_config      ,     fineAcoustic.generation_config		)

			lowerCAmelCase_    :Union[str, Any]			     =  BarkModel(lowercase__		)

			lowerCAmelCase_    :str			     =  semantic
			lowerCAmelCase_    :Union[str, Any]			     =  coarseAcoustic
			lowerCAmelCase_    :Optional[int]			     =  fineAcoustic
			lowerCAmelCase_    :int			     =  codec

			lowerCAmelCase_    :Optional[Any]			     =  bark_generation_config

			Path(lowercase__		).mkdir(exist_ok=lowercase__		)
			bark.save_pretrained(lowercase__      ,     repo_id=lowercase__      ,     push_to_hub=lowercase__		)


if __name__ == "__main__":
				__UpperCAmelCase       =  argparse.ArgumentParser()
				# Required parameters

				parser.add_argument('model_type', type=str, help='text, coarse or fine.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

				__UpperCAmelCase       =  parser.parse_args()

				load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)














import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__				   =		logging.getLogger()
def 						_a    (      )							->			Optional[int]:
 a		  =      argparse.ArgumentParser()
 parser.add_argument('''-f''' )
 a		  =      parser.parse_args()
 return args.f
def 						_a    (      a :Any )							->			Tuple:
 a		  =      {}
 a		  =      os.path.join(a	,							'''all_results.json''' )
 if os.path.exists(a ):
  with open(a	,							'''r''' ) as f:
   a		  =      json.load(a )
 else:
  raise ValueError(F"""can't find {path}""" )
 return results
def 						_a    (      )							->			int:
 a		  =      torch.cuda.is_available() and torch_device == '''cuda'''
 return is_using_cuda and is_apex_available()


UpperCAmelCase__				   =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @classmethod
      def       __lowerCAmelCase					(      cls      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      tempfile.mkdtemp()
       a		  =      os.path.join(cls.tmpdir ,					'''default_config.yml'''  )
       write_basic_config(save_location=cls.configPath  )
       a		  =      ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







      @classmethod
      def       __lowerCAmelCase					(      cls      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       shutil.rmtree(cls.tmpdir  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       if is_cuda_and_apex_available():
        testargs.append('''--fp16'''  )

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.75  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''glue_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       if torch.cuda.device_count() > 1:
        # Skipping because there are not enough batches to train the model + would need a drop_last to work.
        return

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertLess(result['''perplexity'''] ,					100  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''clm_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertLess(result['''perplexity'''] ,					42  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''mlm_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->str:




       """simple docstring"""


       a		  =      7 if get_gpu_count() > 1 else 2

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.75  )
       self.assertLess(result['''train_loss'''] ,					0.5  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''ner_no_trainer'''  )  )  )







      @unittest.skip(reason='''Fix me @muellerzr'''  )
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result['''eval_f1'''] ,					28  )
       self.assertGreaterEqual(result['''eval_exact'''] ,					28  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''qa_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.8  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''swag_no_trainer'''  )  )  )







      @slow
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_rouge1'''] ,					10  )
       self.assertGreaterEqual(result['''eval_rouge2'''] ,					2  )
       self.assertGreaterEqual(result['''eval_rougeL'''] ,					7  )
       self.assertGreaterEqual(result['''eval_rougeLsum'''] ,					7  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''summarization_no_trainer'''  )  )  )







      @slow
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_bleu'''] ,					30  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''translation_no_trainer'''  )  )  )







      @slow
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->int:




       """simple docstring"""


       a		  =      logging.StreamHandler(sys.stdout  )
       logger.addHandler(__UpperCAmelCase  )

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,					0.10  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

       if is_cuda_and_apex_available():
        testargs.append('''--fp16'''  )

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       # The base model scores a 25%
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.6  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''step_1'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''image_classification_no_trainer'''  )  )  )


