



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)

A_		:     Dict        =     logging.getLogger()


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		argparse.ArgumentParser()
 parser.add_argument("-f"					)
 a						=		parser.parse_args()
 return args.f


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 a						=		{}
 a						=		os.path.join(UpperCAmelCase__ ,					"all_results.json"					)
 if os.path.exists(UpperCAmelCase__					):
  with open(UpperCAmelCase__ ,					"r"					) as f:
   a						=		json.load(UpperCAmelCase__					)
 else:
  raise ValueError(F"""can't find {path}"""					)
 return results


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		torch.cuda.is_available() and torch_device == "cuda"
 return is_using_cuda and is_apex_available()


A_		:     Tuple        =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 		_lowercase		(       UpperCAmelCase__	):

   @classmethod
   def 		A							(      cls			:       Union[str, Any]							)       ->				List[Any]:


    """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 		A							(      cls			:       List[Any]							)       ->				Optional[Any]:


    """simple docstring"""






    shutil.rmtree(cls.tmpdir							)

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


    """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(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.7_5							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "glue_no_trainer"							)							)							)

   @mock.patch.dict(os.environ							,   {"WANDB_MODE": "offline"}							)
   def 		A							(      self			:       List[str]							)       ->				Tuple:


    """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(__lowerCAmelCase							)
    self.assertLess(result["perplexity"]							,   100							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "clm_no_trainer"							)							)							)

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


    """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(__lowerCAmelCase							)
    self.assertLess(result["perplexity"]							,   42							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "mlm_no_trainer"							)							)							)

   @mock.patch.dict(os.environ							,   {"WANDB_MODE": "offline"}							)
   def 		A							(      self			:       Tuple							)       ->				List[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(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.7_5							)
    self.assertLess(result["train_loss"]							,   0.5							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "ner_no_trainer"							)							)							)

   @unittest.skip(reason="Fix me @muellerzr"							)
   @mock.patch.dict(os.environ							,   {"WANDB_MODE": "offline"}							)
   def 		A							(      self			:       Union[str, 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(__lowerCAmelCase							)
    # 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(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "qa_no_trainer"							)							)							)

   @mock.patch.dict(os.environ							,   {"WANDB_MODE": "offline"}							)
   def 		A							(      self			:       Any							)       ->				Optional[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(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.8							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "swag_no_trainer"							)							)							)

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


    """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(__lowerCAmelCase							)
    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(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "summarization_no_trainer"							)							)							)

   @slow
   @mock.patch.dict(os.environ							,   {"WANDB_MODE": "offline"}							)
   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """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(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_bleu"]							,   30							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "epoch_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "translation_no_trainer"							)							)							)

   @slow
   def 		A							(      self			:       Tuple							)       ->				Tuple:


    """simple docstring"""






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

    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(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_overall_accuracy"]							,   0.1_0							)




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


    """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(__lowerCAmelCase							)
    # The base model scores a 25%
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.6							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "step_1"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,   "image_classification_no_trainer"							)							)							)






from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


A_		:     List[str]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''audio_values''', '''audio_mask''']

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Dict=2048							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=[16, 16]							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       Optional[int]=4_4100							,   __lowerCAmelCase			:       int=86							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       str=0.0							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(
        feature_size=__lowerCAmelCase							,   sampling_rate=__lowerCAmelCase							,   padding_value=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    a						=		spectrogram_length
    a						=		num_channels
    a						=		patch_size
    a						=		feature_size // self.patch_size[1]
    a						=		n_fft
    a						=		sampling_rate // hop_length_to_sampling_rate
    a						=		sampling_rate
    a						=		padding_value
    a						=		mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2							,   num_mel_filters=__lowerCAmelCase							,   min_frequency=0.0							,   max_frequency=2_2_0_5_0.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   ).T

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    a						=		spectrogram(
        __lowerCAmelCase							,   window_function(self.n_fft							,   "hann"							)							,   frame_length=self.n_fft							,   hop_length=self.hop_length							,   power=2.0							,   mel_filters=self.mel_filters.T							,   log_mel="dB"							,   db_range=8_0.0							,   )
    a						=		log_spec[:, :-1]
    a						=		log_spec - 2_0.0
    a						=		np.clip(log_spec / 4_0.0							,   -2.0							,   0.0							) + 1.0
    return log_spec




   def __call__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,   __lowerCAmelCase			:       Optional[Union[str, TensorType]] = None							,   __lowerCAmelCase			:       Optional[bool] = True							,   __lowerCAmelCase			:       Optional[int] = None							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				BatchFeature:


    """simple docstring"""






    if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
      raise ValueError(
          "This feature extractor is set to support sampling rate"
          f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
          f""" with {self.sampling_rate} and not {sampling_rate}."""							)
    else:
     logger.warning(
         "It is strongly recommended to pass the `sampling_rate` argument to this function. "
         "Failing to do so can result in silent errors that might be hard to debug."							)

    a						=		isinstance(__lowerCAmelCase							,   np.ndarray							) and len(raw_speech.shape							) > 1
    if is_batched_numpy and len(raw_speech.shape							) > 2:
     raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""							)
    a						=		is_batched_numpy or (
        isinstance(__lowerCAmelCase							,   (list, tuple)							) and (isinstance(raw_speech[0]							,   (np.ndarray, tuple, list)							))
    )
    if is_batched:
     a						=		[np.asarray([speech]							,   dtype=np.floataa							).T for speech in raw_speech]
    elif not is_batched and not isinstance(__lowerCAmelCase							,   np.ndarray							):
     a						=		np.asarray(__lowerCAmelCase							,   dtype=np.floataa							)
    elif isinstance(__lowerCAmelCase							,   np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
     a						=		raw_speech.astype(np.floataa							)
    # always return batch
    if not is_batched:
     a						=		[np.asarray([raw_speech]							).T]

    # Convert audio signals to log mel spectrograms, truncate by time axis
    a						=		[
        self._np_extract_fbank_features(waveform.squeeze()							).T[: self.spectrogram_length] for waveform in raw_speech
    ]
    if isinstance(audio_features[0]							,   __lowerCAmelCase							):
     a						=		[np.asarray(__lowerCAmelCase							,   dtype=np.floataa							) for feature in audio_features]

    # Create audio attention mask
    a						=		max(
        [ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len for feature in audio_features]							)  # The maximum number of audio patches in a batch
    if return_attention_mask:
     a						=		[
         (ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len) * [1]
         + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len) * [0]
         for feature in audio_features
     ]
     a						=		np.array(__lowerCAmelCase							).astype(np.floataa							)

    # convert into correct format for padding
    a						=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
    a						=		np.ones([len(__lowerCAmelCase							), 1, max_time_len, self.feature_size]							).astype(np.floataa							)
    a						=		padded_audio_features * self.padding_value
    for i in range(len(__lowerCAmelCase							)							):
     a						=		audio_features[i]
     a						=		feature

    # return as BatchFeature
    if return_attention_mask:
     a						=		{"audio_values": padded_audio_features, "audio_mask": audio_mask}
    else:
     a						=		{"audio_values": padded_audio_features}

    a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)
    return encoded_inputs

