<commit_before>cimport cython
import numpy as np
from numpy.lib.stride_tricks import as_strided

@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_double_C(double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[:,:] mem_view = <double[:rows,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_double_F(double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])

@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_copy_double_C(const double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[:,:] mem_view = <double[:rows,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_copy_double_F(const double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))


@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_float_C(float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[:,:] mem_view = <float[:rows,:cols]>data
    cdef int itemsize = np.dtype('float').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_float_F(float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('float').itemsize
    return as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])

@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_copy_float_C(const float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[:,:] mem_view = <float[:rows,:cols]>data
    cdef int itemsize = np.dtype('float').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_copy_float_F(const float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('float').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))


@cython.boundscheck(False)
cdef np.ndarray[long, ndim=2] ndarray_long_C(long *data, long rows, long cols, long row_stride, long col_stride):
    cdef long[:,:] mem_view = <long[:rows,:cols]>data
    cdef int itemsize = np.dtype('long').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[long, ndim=2] ndarray_long_F(long *data, long rows, long cols, long row_stride, long col_stride):
    cdef long[::1,:] mem_view = <long[:rows:1,:cols]>data<commit_msg>Change dtype in float conv to float32<commit_after>cimport cython
import numpy as np
from numpy.lib.stride_tricks import as_strided

@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_double_C(double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[:,:] mem_view = <double[:rows,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_double_F(double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])

@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_copy_double_C(const double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[:,:] mem_view = <double[:rows,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
@cython.boundscheck(False)
cdef np.ndarray[double, ndim=2] ndarray_copy_double_F(const double *data, long rows, long cols, long row_stride, long col_stride):
    cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('double').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))


@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_float_C(float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[:,:] mem_view = <float[:rows,:cols]>data
    cdef int itemsize = np.dtype('float32').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_float_F(float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('float32').itemsize
    return as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])

@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_copy_float_C(const float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[:,:] mem_view = <float[:rows,:cols]>data
    cdef int itemsize = np.dtype('float32').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
@cython.boundscheck(False)
cdef np.ndarray[float, ndim=2] ndarray_copy_float_F(const float *data, long rows, long cols, long row_stride, long col_stride):
    cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
    cdef int itemsize = np.dtype('float32').itemsize
    return np.copy(as_strided(np.asarray(mem_view, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))


@cython.boundscheck(False)
cdef np.ndarray[long, ndim=2] ndarray_long_C(long *data, long rows, long cols, long row_stride, long col_stride):
    cdef long[:,:] mem_view = <long[:rows,:cols]>data
    cdef int itemsize = np.dtype('long').itemsize
    return as_strided(np.asarray(mem_view, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
@cython.boundscheck(False)
cdef np.ndarray[long, ndim=2] ndarray_long_F(long *data, long rows, long cols, long row_stride, long col_stride):
    cdef long[::1,:] mem_view = <long[:rows:1,:cols]>data
{'fraction_non_alphanumeric': 0.14570259208731243, 'fraction_numerical': 0.0068212824010914054, 'mean_word_length': 6.823906083244397, 'pattern_counts': {'":': 0, '<': 23, '<?xml version=': 0, '>': 23, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5735430', 'n_tokens_mistral': 2673, 'n_tokens_neox': 2653, 'n_words': 555}