"""simple docstring"""



import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 		lowercase (       UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     Any		):





      """simple docstring"""
      assert isinstance(UpperCamelCase       ,  UpperCamelCase		)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 1
      assert dataset.column_names == ["text"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"       ,  [False, True]		)
def 		lowercase (       UpperCamelCase     :     Any       ,  UpperCamelCase     :     Any       ,  UpperCamelCase     :     str		):





      """simple docstring"""
      A__	:      Optional[Any]      =tmp_path / "cache"
      A__	:      Optional[Any]      ={"text": "string"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            A__	:      List[str]      =TextDatasetReader(UpperCamelCase       ,  cache_dir=UpperCamelCase       ,  keep_in_memory=UpperCamelCase		).read()
      _check_text_dataset(UpperCamelCase       ,  UpperCamelCase		)



@pytest.mark.parametrize(
    "features"       ,  [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]       ,  )
def 		lowercase (       UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     Tuple       ,  UpperCamelCase     :     Any		):





      """simple docstring"""
      A__	:      List[str]      =tmp_path / "cache"
      A__	:      Optional[int]      ={"text": "string"}
      A__	:      Tuple      =features.copy() if features else default_expected_features
      A__	:      List[str]      =(
          Features({feature: Value(UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
      )
      A__	:      int      =TextDatasetReader(UpperCamelCase       ,  features=UpperCamelCase       ,  cache_dir=UpperCamelCase		).read()
      _check_text_dataset(UpperCamelCase       ,  UpperCamelCase		)



@pytest.mark.parametrize("split"       ,  [None, NamedSplit("train"		), "train", "test"]		)
def 		lowercase (       UpperCamelCase     :     Optional[Any]       ,  UpperCamelCase     :     Tuple       ,  UpperCamelCase     :     Optional[Any]		):





      """simple docstring"""
      A__	:      List[str]      =tmp_path / "cache"
      A__	:      Union[str, Any]      ={"text": "string"}
      A__	:      str      =TextDatasetReader(UpperCamelCase       ,  cache_dir=UpperCamelCase       ,  split=UpperCamelCase		).read()
      _check_text_dataset(UpperCamelCase       ,  UpperCamelCase		)
      assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"       ,  [str, list]		)
def 		lowercase (       UpperCamelCase     :     Any       ,  UpperCamelCase     :     str       ,  UpperCamelCase     :     str		):





      """simple docstring"""
      if issubclass(UpperCamelCase       ,  UpperCamelCase		):
            A__	:      Any      =text_path
      elif issubclass(UpperCamelCase       ,  UpperCamelCase		):
            A__	:      int      =[text_path]
      A__	:      Dict      =tmp_path / "cache"
      A__	:      Optional[int]      ={"text": "string"}
      A__	:      Any      =TextDatasetReader(UpperCamelCase       ,  cache_dir=UpperCamelCase		).read()
      _check_text_dataset(UpperCamelCase       ,  UpperCamelCase		)



def 		lowercase (       UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     int=("train",)		):





      """simple docstring"""
      assert isinstance(UpperCamelCase       ,  UpperCamelCase		)
      for split in splits:
            A__	:      Optional[Any]      =dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 1
            assert dataset.column_names == ["text"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"       ,  [False, True]		)
def 		lowercase (       UpperCamelCase     :     int       ,  UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     Any		):





      """simple docstring"""
      A__	:      Dict      =tmp_path / "cache"
      A__	:      Optional[int]      ={"text": "string"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            A__	:      Union[str, Any]      =TextDatasetReader({"train": text_path}       ,  cache_dir=UpperCamelCase       ,  keep_in_memory=UpperCamelCase		).read()
      _check_text_datasetdict(UpperCamelCase       ,  UpperCamelCase		)



@pytest.mark.parametrize(
    "features"       ,  [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]       ,  )
def 		lowercase (       UpperCamelCase     :     Dict       ,  UpperCamelCase     :     Optional[Any]       ,  UpperCamelCase     :     Any		):





      """simple docstring"""
      A__	:      Union[str, Any]      =tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      A__	:      Optional[int]      ={"text": "string"}
      A__	:      Optional[int]      =features.copy() if features else default_expected_features
      A__	:      Union[str, Any]      =(
          Features({feature: Value(UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
      )
      A__	:      int      =TextDatasetReader({"train": text_path}       ,  features=UpperCamelCase       ,  cache_dir=UpperCamelCase		).read()
      _check_text_datasetdict(UpperCamelCase       ,  UpperCamelCase		)



@pytest.mark.parametrize("split"       ,  [None, NamedSplit("train"		), "train", "test"]		)
def 		lowercase (       UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     Optional[int]		):





      """simple docstring"""
      if split:
            A__	:      Optional[int]      ={split: text_path}
      else:
            A__	:      Tuple      ="train"
            A__	:      Dict      ={"train": text_path, "test": text_path}
      A__	:      Dict      =tmp_path / "cache"
      A__	:      str      ={"text": "string"}
      A__	:      Optional[int]      =TextDatasetReader(UpperCamelCase       ,  cache_dir=UpperCamelCase		).read()
      _check_text_datasetdict(UpperCamelCase       ,  UpperCamelCase       ,  splits=list(path.keys()		)		)
      assert all(dataset[split].split == split for split in path.keys()		)









'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__UpperCAmelCase       							=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__UpperCAmelCase       							=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__UpperCAmelCase       							=				dict(zip(vocab, range(len(vocab))))
__UpperCAmelCase       							=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
		__UpperCAmelCase       							=				Path(tmpdirname)
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
		with open(src_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(tgt_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(merges_file, '''w''') as fp:
				fp.write('''\n'''.join(merges))

		__UpperCAmelCase       							=				FSMTTokenizer(
		    langs=['''en''', '''ru'''],
		    src_vocab_size=len(vocab),
		    tgt_vocab_size=len(vocab),
		    src_vocab_file=src_vocab_file,
		    tgt_vocab_file=tgt_vocab_file,
		    merges_file=merges_file,
		)

__UpperCAmelCase       							=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__UpperCAmelCase       							=				FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__UpperCAmelCase       							=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCAmelCase       							=				tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru