From 12cf076118570eebbff08c6b3090e0d4798447a1 Mon Sep 17 00:00:00 2001
From: cyfraeviolae <cyfraeviolae>
Date: Wed, 3 Apr 2024 03:17:55 -0400
Subject: no venv

---
 .../site-packages/faker/utils/distribution.py      | 88 ----------------------
 1 file changed, 88 deletions(-)
 delete mode 100644 venv/lib/python3.11/site-packages/faker/utils/distribution.py

(limited to 'venv/lib/python3.11/site-packages/faker/utils/distribution.py')

diff --git a/venv/lib/python3.11/site-packages/faker/utils/distribution.py b/venv/lib/python3.11/site-packages/faker/utils/distribution.py
deleted file mode 100644
index 45580a5..0000000
--- a/venv/lib/python3.11/site-packages/faker/utils/distribution.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import bisect
-import itertools
-
-from random import Random
-from typing import Generator, Iterable, Optional, Sequence, TypeVar
-
-from faker.generator import random as mod_random
-
-
-def random_sample(random: Optional[Random] = None) -> float:
-    if random is None:
-        random = mod_random
-    return random.uniform(0.0, 1.0)
-
-
-def cumsum(it: Iterable[float]) -> Generator[float, None, None]:
-    total: float = 0
-    for x in it:
-        total += x
-        yield total
-
-
-T = TypeVar("T")
-
-
-def choices_distribution_unique(
-    a: Sequence[T],
-    p: Optional[Sequence[float]],
-    random: Optional[Random] = None,
-    length: int = 1,
-) -> Sequence[T]:
-    # As of Python 3.7, there isn't a way to sample unique elements that takes
-    # weight into account.
-    if random is None:
-        random = mod_random
-
-    assert p is not None
-    assert len(a) == len(p)
-    assert len(a) >= length, "You can't request more unique samples than elements in the dataset."
-
-    choices = []
-    items = list(a)
-    probabilities = list(p)
-    for i in range(length):
-        cdf = tuple(cumsum(probabilities))
-        normal = cdf[-1]
-        cdf2 = [i / normal for i in cdf]
-        uniform_sample = random_sample(random=random)
-        idx = bisect.bisect_right(cdf2, uniform_sample)
-        item = items[idx]
-        choices.append(item)
-        probabilities.pop(idx)
-        items.pop(idx)
-    return choices
-
-
-def choices_distribution(
-    a: Sequence[T],
-    p: Optional[Sequence[float]],
-    random: Optional[Random] = None,
-    length: int = 1,
-) -> Sequence[T]:
-    if random is None:
-        random = mod_random
-
-    if p is not None:
-        assert len(a) == len(p)
-
-    if hasattr(random, "choices"):
-        if length == 1 and p is None:
-            return [random.choice(a)]
-        else:
-            return random.choices(a, weights=p, k=length)
-    else:
-        choices = []
-
-        if p is None:
-            p = itertools.repeat(1, len(a))  # type: ignore
-
-        cdf = list(cumsum(p))  # type: ignore
-        normal = cdf[-1]
-        cdf2 = [i / normal for i in cdf]
-        for i in range(length):
-            uniform_sample = random_sample(random=random)
-            idx = bisect.bisect_right(cdf2, uniform_sample)
-            item = a[idx]
-            choices.append(item)
-        return choices
-- 
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