

"""simple docstring"""





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers import AutoModelForCausalLM




@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
    __lowercase								=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase__	)
    __lowercase								=						-1

    __lowercase								=						ids_tensor((1, 5),					vocab_size=model.config.vocab_size	).to(UpperCAmelCase__	)
    __lowercase								=						model.generate(UpperCAmelCase__,					max_new_tokens=1_0,					do_sample=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(greedy_ids[0]	)

    with CaptureStdout() as cs:
       __lowercase								=						TextStreamer(UpperCAmelCase__	)
       model.generate(UpperCAmelCase__,					max_new_tokens=1_0,					do_sample=UpperCAmelCase__,					streamer=UpperCAmelCase__	)
    # The greedy text should be printed to stdout, except for the final "\n" in the streamer
    __lowercase								=						cs.out[:-1]

    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
    __lowercase								=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase__	)
    __lowercase								=						-1

    __lowercase								=						ids_tensor((1, 5),					vocab_size=model.config.vocab_size	).to(UpperCAmelCase__	)
    __lowercase								=						model.generate(UpperCAmelCase__,					max_new_tokens=1_0,					do_sample=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(greedy_ids[0]	)

    __lowercase								=						TextIteratorStreamer(UpperCAmelCase__	)
    __lowercase								=						{"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
    __lowercase								=						Thread(target=model.generate,					kwargs=UpperCAmelCase__	)
    thread.start()
    __lowercase								=						""
    for new_text in streamer:
       streamer_text += new_text

    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
    __lowercase								=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase__	)
    __lowercase								=						-1

    __lowercase								=						ids_tensor((1, 5),					vocab_size=model.config.vocab_size	).to(UpperCAmelCase__	)
    __lowercase								=						model.generate(UpperCAmelCase__,					max_new_tokens=1_0,					do_sample=UpperCAmelCase__	)
    __lowercase								=						greedy_ids[:, input_ids.shape[1] :]
    __lowercase								=						tokenizer.decode(new_greedy_ids[0]	)

    with CaptureStdout() as cs:
       __lowercase								=						TextStreamer(UpperCAmelCase__,					skip_prompt=UpperCAmelCase__	)
       model.generate(UpperCAmelCase__,					max_new_tokens=1_0,					do_sample=UpperCAmelCase__,					streamer=UpperCAmelCase__	)
    # The greedy text should be printed to stdout, except for the final "\n" in the streamer
    __lowercase								=						cs.out[:-1]

    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
    # with actual models -- the dummy models' tokenizers are not aligned with their models, and
    # `skip_special_tokens=True` has no effect on them
    __lowercase								=						AutoTokenizer.from_pretrained("distilgpt2"	)
    __lowercase								=						AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(UpperCAmelCase__	)
    __lowercase								=						-1

    __lowercase								=						torch.ones((1, 5),					device=UpperCAmelCase__	).long() * model.config.bos_token_id
    with CaptureStdout() as cs:
       __lowercase								=						TextStreamer(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)
       model.generate(UpperCAmelCase__,					max_new_tokens=1,					do_sample=UpperCAmelCase__,					streamer=UpperCAmelCase__	)

    # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
    # re-tokenized, must only contain one token
    __lowercase								=						cs.out[:-1]  # Remove the final "\n"
    __lowercase								=						tokenizer(UpperCAmelCase__,					return_tensors="pt"	)
    self.assertEqual(streamer_text_tokenized.input_ids.shape,					(1, 1)	)



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
    __lowercase								=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase__	)
    __lowercase								=						-1

    __lowercase								=						ids_tensor((1, 5),					vocab_size=model.config.vocab_size	).to(UpperCAmelCase__	)
    __lowercase								=						TextIteratorStreamer(UpperCAmelCase__,					timeout=0.001	)
    __lowercase								=						{"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
    __lowercase								=						Thread(target=model.generate,					kwargs=UpperCAmelCase__	)
    thread.start()

    # The streamer will timeout after 0.001 seconds, so an exception will be raised
    with self.assertRaises(UpperCAmelCase__	):
       __lowercase								=						""
       for new_text in streamer:
          streamer_text += new_text







"""simple docstring"""





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


_a       =							datasets.utils.logging.get_logger(__name__)

_a       =							['names', 'prefix']
_a       =							['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
_a       =							['encoding_errors', 'on_bad_lines']
_a       =							['date_format']




@dataclass
class 					_lowerCAmelCase  (	datasets.BuilderConfig   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	","
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, List[int], str]]   		=	"infer"
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, str, List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[Union[List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[Literal["c", "python", "pyarrow"]]   		=	None
 __UpperCAmelCase	:					Dict[Union[int, str], Callable[[Any], Any]]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[Union[int, List[int]]]   		=	None
 __UpperCAmelCase	:					Optional[int]   		=	None
 __UpperCAmelCase	:					Optional[Union[str, List[str]]]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	"."
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	'"'
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					int   		=	1_0_0_0_0
 __UpperCAmelCase	:					Optional[datasets.Features]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	"strict"
 __UpperCAmelCase	:					Literal["error", "warn", "skip"]   		=	"error"
 __UpperCAmelCase	:					Optional[str]   		=	None


 def 					_lowercase (			self							:			Tuple	):
    if self.delimiter is not None:
       __lowercase								=						self.delimiter
    if self.column_names is not None:
       __lowercase								=						self.column_names



 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						{
        "sep": self.sep,
        "header": self.header,
        "names": self.names,
        "index_col": self.index_col,
        "usecols": self.usecols,
        "prefix": self.prefix,
        "mangle_dupe_cols": self.mangle_dupe_cols,
        "engine": self.engine,
        "converters": self.converters,
        "true_values": self.true_values,
        "false_values": self.false_values,
        "skipinitialspace": self.skipinitialspace,
        "skiprows": self.skiprows,
        "nrows": self.nrows,
        "na_values": self.na_values,
        "keep_default_na": self.keep_default_na,
        "na_filter": self.na_filter,
        "verbose": self.verbose,
        "skip_blank_lines": self.skip_blank_lines,
        "thousands": self.thousands,
        "decimal": self.decimal,
        "lineterminator": self.lineterminator,
        "quotechar": self.quotechar,
        "quoting": self.quoting,
        "escapechar": self.escapechar,
        "comment": self.comment,
        "encoding": self.encoding,
        "dialect": self.dialect,
        "error_bad_lines": self.error_bad_lines,
        "warn_bad_lines": self.warn_bad_lines,
        "skipfooter": self.skipfooter,
        "doublequote": self.doublequote,
        "memory_map": self.memory_map,
        "float_precision": self.float_precision,
        "chunksize": self.chunksize,
        "encoding_errors": self.encoding_errors,
        "on_bad_lines": self.on_bad_lines,
        "date_format": self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
       if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig(),					UpperCAmelCase__	):
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs







class 					_lowerCAmelCase  (	datasets.ArrowBasedBuilder   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	CsvConfig


 def 					_lowercase (			self							:			List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict	):
    if not self.config.data_files:
       raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""	)
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files	)
    if isinstance(UpperCAmelCase__,					(str, list, tuple)	):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"files": files}	)]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__,					gen_kwargs={"files": files}	)	)
    return splits


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			pa.Table	):
    if self.config.features is not None:
       __lowercase								=						self.config.features.arrow_schema
       if all(not require_storage_cast(UpperCAmelCase__	) for feature in self.config.features.values()	):
          # cheaper cast
          __lowercase								=						pa.Table.from_arrays([pa_table[field.name] for field in schema],					schema=UpperCAmelCase__	)
       else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          __lowercase								=						table_cast(UpperCAmelCase__,					UpperCAmelCase__	)
    return pa_table



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __lowercase								=						(
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCAmelCase__	) else object
            for name, dtype, feature in zip(schema.names,					schema.types,					self.config.features.values()	)
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__	)	):
       __lowercase								=						pd.read_csv(UpperCAmelCase__,					iterator=UpperCAmelCase__,					dtype=UpperCAmelCase__,					**self.config.pd_read_csv_kwargs	)
       try:
          for batch_idx, df in enumerate(UpperCAmelCase__	):
             __lowercase								=						pa.Table.from_pandas(UpperCAmelCase__	)
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__	)
       except ValueError as e:
          logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase__	)}: {e}"""	)
          raise




