


"""simple docstring"""







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     from .features import FeatureType


lowerCAmelCase__    ,lowerCAmelCase__    ,lowerCAmelCase__				    =       False, False, False


@dataclass
class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       a  :    Optional[int]       =None
       a  :    bool       =True
       a  :    bool       =True
       a  :    Optional[str]       =None
       # Automatically constructed
       a  :    ClassVar[str]       ="dict"
       a  :    ClassVar[Any]       =pa.struct({"bytes": pa.binary(), "path": pa.string()}    )
       a  :    str       =field(default="Audio"       ,   init=lowercase       ,   repr=lowercase    )

       def __call__(						self						):






         """simple docstring"""





         return self.pa_type

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         try:
           import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
         except ImportError as err:
           raise ImportError("To support encoding audio data, please install 'soundfile'."						) from err
         if isinstance(snake_case__		,					snake_case__						):
           return {"bytes": None, "path": value}
         elif isinstance(snake_case__		,					snake_case__						):
           return {"bytes": value, "path": None}
         elif "array" in value:
           # convert the audio array to wav bytes
           lowerCAmelCase					:					List[str]								=							BytesIO()
           sf.write(snake_case__		,					value["array"]		,					value["sampling_rate"]		,					format="wav"						)
           return {"bytes": buffer.getvalue(), "path": None}
         elif value.get("path"						) is not None and os.path.isfile(value["path"]						):
           # we set "bytes": None to not duplicate the data if they're already available locally
           if value["path"].endswith("pcm"						):
             # "PCM" only has raw audio bytes
             if value.get("sampling_rate"						) is None:
               # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
               raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"						)
             if value.get("bytes"						):
               # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
               lowerCAmelCase					:					Any								=							np.frombuffer(value["bytes"]		,					dtype=np.intaa						).astype(np.floataa						) / 32_767
             else:
               lowerCAmelCase					:					List[str]								=							np.memmap(value["path"]		,					dtype="h"		,					mode="r"						).astype(np.floataa						) / 32_767

             lowerCAmelCase					:					Optional[int]								=							BytesIO(bytes()						)
             sf.write(snake_case__		,					snake_case__		,					value["sampling_rate"]		,					format="wav"						)
             return {"bytes": buffer.getvalue(), "path": None}
           else:
             return {"bytes": None, "path": value.get("path"						)}
         elif value.get("bytes"						) is not None or value.get("path"						) is not None:
           # store the audio bytes, and path is used to infer the audio format using the file extension
           return {"bytes": value.get("bytes"						), "path": value.get("path"						)}
         else:
           raise ValueError(
               f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         if not self.decode:
           raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."						)

         lowerCAmelCase						,  lowerCAmelCase					:					Dict								=							(value["path"], BytesIO(value["bytes"]						)) if value["bytes"] is not None else (value["path"], None)
         if path is None and file is None:
           raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""						)

         try:
           import librosa
           import soundfile as sf
         except ImportError as err:
           raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."						) from err

         lowerCAmelCase					:					Any								=							xsplitext(snake_case__						)[1][1:].lower() if path is not None else None
         if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
           raise RuntimeError(
               "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
               "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "						)
         elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
           raise RuntimeError(
               "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
               "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "						)

         if file is None:
           lowerCAmelCase					:					str								=							token_per_repo_id or {}
           lowerCAmelCase					:					str								=							path.split("::"						)[-1]
           try:
             lowerCAmelCase					:					Union[str, Any]								=							string_to_dict(snake_case__		,					config.HUB_DATASETS_URL						)["repo_id"]
             lowerCAmelCase					:					Union[str, Any]								=							token_per_repo_id[repo_id]
           except (ValueError, KeyError):
             lowerCAmelCase					:					Dict								=							None

           with xopen(snake_case__		,					"rb"		,					use_auth_token=snake_case__						) as f:
             lowerCAmelCase						,  lowerCAmelCase					:					Dict								=							sf.read(snake_case__						)

         else:
           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sf.read(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							array.T
         if self.mono:
           lowerCAmelCase					:					Dict								=							librosa.to_mono(snake_case__						)
         if self.sampling_rate and self.sampling_rate != sampling_rate:
           lowerCAmelCase					:					List[str]								=							librosa.resample(snake_case__		,					orig_sr=snake_case__		,					target_sr=self.sampling_rate						)
           lowerCAmelCase					:					Optional[Any]								=							self.sampling_rate

         return {"path": path, "array": array, "sampling_rate": sampling_rate}

       def 	lowercase__							(						self						):






         """simple docstring"""





         from .features import Value

         if self.decode:
           raise ValueError("Cannot flatten a decoded Audio feature."						)
         return {
             "bytes": Value("binary"						),
             "path": Value("string"						),
         }

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         if pa.types.is_string(storage.type						):
           lowerCAmelCase					:					Any								=							pa.array([None] * len(snake_case__						)		,					type=pa.binary()						)
           lowerCAmelCase					:					Tuple								=							pa.StructArray.from_arrays([bytes_array, storage]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         elif pa.types.is_binary(storage.type						):
           lowerCAmelCase					:					str								=							pa.array([None] * len(snake_case__						)		,					type=pa.string()						)
           lowerCAmelCase					:					List[Any]								=							pa.StructArray.from_arrays([storage, path_array]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         elif pa.types.is_struct(storage.type						) and storage.type.get_all_field_indices("array"						):
           lowerCAmelCase					:					Dict								=							pa.array([Audio().encode_example(snake_case__						) if x is not None else None for x in storage.to_pylist()]						)
         elif pa.types.is_struct(storage.type						):
           if storage.type.get_field_index("bytes"						) >= 0:
             lowerCAmelCase					:					Tuple								=							storage.field("bytes"						)
           else:
             lowerCAmelCase					:					Tuple								=							pa.array([None] * len(snake_case__						)		,					type=pa.binary()						)
           if storage.type.get_field_index("path"						) >= 0:
             lowerCAmelCase					:					Dict								=							storage.field("path"						)
           else:
             lowerCAmelCase					:					List[str]								=							pa.array([None] * len(snake_case__						)		,					type=pa.string()						)
           lowerCAmelCase					:					int								=							pa.StructArray.from_arrays([bytes_array, path_array]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         return array_cast(snake_case__		,					self.pa_type						)







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         @no_op_if_value_is_null
         def path_to_bytes(snake_case__						):
           with xopen(snake_case__		,					"rb"						) as f:
             lowerCAmelCase					:					Tuple								=							f.read()
           return bytes_

         lowerCAmelCase					:					str								=							pa.array(
             [
                 (path_to_bytes(x["path"]						) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]		,					type=pa.binary()		,					)
         lowerCAmelCase					:					List[str]								=							pa.array(
             [os.path.basename(snake_case__						) if path is not None else None for path in storage.field("path"						).to_pylist()]		,					type=pa.string()		,					)
         lowerCAmelCase					:					str								=							pa.StructArray.from_arrays([bytes_array, path_array]		,					["bytes", "path"]		,					mask=bytes_array.is_null()						)
         return array_cast(snake_case__		,					self.pa_type						)









"""simple docstring"""







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)

lowerCAmelCase__				    =       logging.getLogger()
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							argparse.ArgumentParser()
  parser.add_argument("-f"    )
  lowerCAmelCase					:					Tuple								=							parser.parse_args()
  return args.f
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					Union[str, Any]								=							{}
  lowerCAmelCase					:					List[str]								=							os.path.join(SCREAMING_SNAKE_CASE    ,				"all_results.json"    )
  if os.path.exists(SCREAMING_SNAKE_CASE    ):
    with open(SCREAMING_SNAKE_CASE    ,				"r"    ) as f:
      lowerCAmelCase					:					str								=							json.load(SCREAMING_SNAKE_CASE    )
  else:
    raise ValueError(f"""can't find {path}"""    )
  return results
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							torch.cuda.is_available() and torch_device == "cuda"
  return is_using_cuda and is_apex_available()


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





class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       @classmethod
       def 	lowercase__							(						cls						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							tempfile.mkdtemp()
         lowerCAmelCase					:					List[Any]								=							os.path.join(cls.tmpdir		,					"default_config.yml"						)
         write_basic_config(save_location=cls.configPath						)
         lowerCAmelCase					:					Optional[int]								=							["accelerate", "launch", "--config_file", cls.configPath]

       @classmethod
       def 	lowercase__							(						cls						):






         """simple docstring"""





         shutil.rmtree(cls.tmpdir						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					int								=							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						)
         lowerCAmelCase					:					Dict								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_accuracy"]		,					0.75						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"glue_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					List[str]								=							get_results(snake_case__						)
         self.assertLess(result["perplexity"]		,					100						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"clm_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					Any								=							get_results(snake_case__						)
         self.assertLess(result["perplexity"]		,					42						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"mlm_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							7 if get_gpu_count() > 1 else 2

         lowerCAmelCase					:					Optional[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					int								=							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						)
         lowerCAmelCase					:					str								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_accuracy"]		,					0.75						)
         self.assertLess(result["train_loss"]		,					0.5						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"ner_no_trainer"						)						)						)

       @unittest.skip(reason="Fix me @muellerzr"						)
       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					List[str]								=							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						)
         lowerCAmelCase					:					Optional[Any]								=							get_results(snake_case__						)
         # 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(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"qa_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					List[Any]								=							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						)
         lowerCAmelCase					:					Tuple								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_accuracy"]		,					0.8						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"swag_no_trainer"						)						)						)

       @slow
       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					str								=							get_results(snake_case__						)
         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(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"summarization_no_trainer"						)						)						)

       @slow
       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					int								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_bleu"]		,					30						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"translation_no_trainer"						)						)						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							logging.StreamHandler(sys.stdout						)
         logger.addHandler(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Any								=							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						)
         lowerCAmelCase					:					int								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_overall_accuracy"]		,					0.10						)







       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Union[str, Any]								=							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						)
         lowerCAmelCase					:					Dict								=							get_results(snake_case__						)
         # The base model scores a 25%
         self.assertGreaterEqual(result["eval_accuracy"]		,					0.6						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"step_1"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"image_classification_no_trainer"						)						)						)





