




'''simple docstring'''

import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						Dataset.from_dict({"""i""": range(17	)}	)
			snake_case__          =						len(a	)
			snake_case__          =						3
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert sum(len(a	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			def gen():
						return ({"i": i} for i in range(17	))

			snake_case__          =						3
			snake_case__          =						IterableDataset.from_generator(a	)
			snake_case__          =						len(list(a	)	)
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert sum(len(list(a	)	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




@pytest.mark.parametrize("""shards_per_node"""       ,      [1, 2, 3]	)
def    snake_case__     (  a	)			->       int:





			'''simple docstring'''






			def gen(a	):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17	))

			snake_case__          =						3
			snake_case__          =						shards_per_node * world_size
			snake_case__          =						{"""shards""": [F"""shard_{shard_idx}.txt""" for shard_idx in range(a	)]}
			snake_case__          =						IterableDataset.from_generator(a       ,      gen_kwargs=a	)
			snake_case__          =						len(list(a	)	)
			assert full_ds.n_shards == world_size * shards_per_node
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(a	)	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




def    snake_case__     (  )			->       List[str]:





			'''simple docstring'''






			def gen():
						return ({"i": i} for i in range(17	))

			snake_case__          =						2
			snake_case__          =						IterableDataset.from_generator(a	)
			snake_case__          =						len(list(a	)	)

			snake_case__          =						split_dataset_by_node(a       ,      rank=0       ,      world_size=a	).shuffle(seed=42	)
			assert len(list(a	)	) == 1 + full_size // world_size
			with pytest.raises(a	):
						split_dataset_by_node(a       ,      rank=0       ,      world_size=a	).shuffle()

			snake_case__          =						split_dataset_by_node(full_ds.shuffle(seed=42	)       ,      rank=0       ,      world_size=a	)
			assert len(list(a	)	) == 1 + full_size // world_size
			with pytest.raises(a	):
						split_dataset_by_node(full_ds.shuffle()       ,      rank=0       ,      world_size=a	)




@pytest.mark.parametrize("""streaming"""       ,      [False, True]	)
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,      reason="""execute_subprocess_async doesn't support windows"""	)
@pytest.mark.integration
def    snake_case__     (  a	)			->       str:





			'''simple docstring'''






			snake_case__          =						2
			snake_case__          =						get_torch_dist_unique_port()
			snake_case__          =						Path(a	).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			snake_case__          =						F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			snake_case__          =						F"""
        --streaming={streaming}
    """.split()
			snake_case__          =						[sys.executable] + distributed_args + args
			execute_subprocess_async(a       ,      env=os.environ.copy()	)




@pytest.mark.parametrize(
    """nproc_per_node, num_workers"""       ,      [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]       ,      )
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,      reason="""execute_subprocess_async doesn't support windows"""	)
@pytest.mark.integration
def    snake_case__     (  a       ,      a	)			->       int:





			'''simple docstring'''






			snake_case__          =						True
			snake_case__          =						get_torch_dist_unique_port()
			snake_case__          =						Path(a	).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			snake_case__          =						F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			snake_case__          =						F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			snake_case__          =						[sys.executable] + distributed_args + args
			execute_subprocess_async(a       ,      env=os.environ.copy()	)





import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


a_ 		=  get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    SpeechTaTokenizer(snake_case					)
          tokenizer.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''do_normalize''': False,
              '''num_mel_bins''': 80,
              '''hop_length''': 16,
              '''win_length''': 64,
              '''win_function''': '''hann_window''',
              '''fmin''': 80,
              '''fmax''': 7600,
              '''mel_floor''': 1E-10,
              '''reduction_factor''': 2,
              '''return_attention_mask''': True,
          }

          __UpperCamelCase			=    os.path.join(self.tmpdirname			,							snake_case					)
          with open(self.feature_extraction_file			,							'''w'''			,							encoding='''utf-8'''					) as fp:
              fp.write(json.dumps(snake_case					) + '''\n'''					)







      def    snake_case						(  self       :			Union[str, Any]			,							**snake_case       :			List[Any]					):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			List[Any]			,							**snake_case       :			List[str]					):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_tokenizer()
          __UpperCamelCase			=    self.get_feature_extractor()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          processor.save_pretrained(self.tmpdirname					)
          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(self.tmpdirname					)

          self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()					)
          self.assertIsInstance(processor.tokenizer			,							snake_case					)

          self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor.to_json_string()					)
          self.assertIsInstance(processor.feature_extractor			,							snake_case					)







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=self.get_tokenizer()			,							feature_extractor=self.get_feature_extractor()					)
          processor.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''					)
          __UpperCamelCase			=    self.get_feature_extractor(do_normalize=snake_case			,							padding_value=1.0					)

          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

          self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()					)
          self.assertIsInstance(processor.tokenizer			,							snake_case					)

          self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor_add_kwargs.to_json_string()					)
          self.assertIsInstance(processor.feature_extractor			,							snake_case					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio=snake_case			,							return_tensors='''np'''					)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1E-2					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio_target=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio_target=snake_case			,							return_tensors='''np'''					)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1E-2					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text_target=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          self.assertListEqual(
              processor.model_input_names			,							feature_extractor.model_input_names			,							msg='''`processor` and `feature_extractor` model input names do not match'''			,							)






