prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Cut concatenate doesn't consider the first sample in each batch
  Found in #234 
  
  </issue>
  <code>
  [start of lhotse/dataset/cut_transforms/concatenate.py]
  1 from typing import Optional, Sequence
  2 
  3 from lhotse import CutSet
  4 from lhotse.cut import AnyCut
  5 from lhotse.utils import Seconds
  6 
  7 
  8 class CutConcatenate:
  9     """
  10     A transform on batch of cuts (``CutSet``) that concatenates the cuts to minimize the total amount of padding;
  11     e.g. instead of creating a batch with 40 examples, we will merge some of the examples together
  12     adding some silence between them to avoid a large number of padding frames that waste the computation.
  13     """
  14 
  15     def __init__(
  16             self,
  17             gap: Seconds = 1.0,
  18             duration_factor: float = 1.0
  19     ) -> None:
  20         """
  21         CutConcatenate's constructor.
  22 
  23         :param gap: The duration of silence in seconds that is inserted between the cuts;
  24             it's goal is to let the model "know" that there are separate utterances in a single example.
  25         :param duration_factor: Determines the maximum duration of the concatenated cuts;
  26             by default it's 1, setting the limit at the duration of the longest cut in the batch.
  27         """
  28         self.gap = gap
  29         self.duration_factor = duration_factor
  30 
  31     def __call__(self, cuts: CutSet) -> CutSet:
  32         cuts = cuts.sort_by_duration(ascending=False)
  33         return concat_cuts(
  34             cuts,
  35             gap=self.gap,
  36             max_duration=cuts[0].duration * self.duration_factor
  37         )
  38 
  39 
  40 def concat_cuts(
  41         cuts: Sequence[AnyCut],
  42         gap: Seconds = 1.0,
  43         max_duration: Optional[Seconds] = None
  44 ) -> CutSet:
  45     """
  46     We're going to concatenate the cuts to minimize the amount of total padding frames used.
  47     This means that some samples in the batch will be merged together into one sample,
  48     separated by an interval of silence.
  49     This is actually solving a knapsack problem.
  50     In this initial implementation we're using a greedy approach:
  51     going from the back (i.e. the shortest cuts) we'll try to concat them to the longest cut
  52     that still has some "space" at the end.
  53 
  54     :param cuts: a list of cuts to pack.
  55     :param gap: the duration of silence inserted between concatenated cuts.
  56     :param max_duration: the maximum duration for the concatenated cuts
  57         (by default set to the duration of the first cut).
  58     :return a list of packed cuts.
  59     """
  60     if len(cuts) <= 1:
  61         # Nothing to do.
  62         return CutSet.from_cuts(cuts)
  63     cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)
  64     max_duration = cuts[0].duration if max_duration is None else max_duration
  65     current_idx = 1
  66     while True:
  67         can_fit = False
  68         shortest = cuts[-1]
  69         for idx in range(current_idx, len(cuts) - 1):
  70             cut = cuts[current_idx]
  71             can_fit = cut.duration + gap + shortest.duration <= max_duration
  72             if can_fit:
  73                 cuts[current_idx] = cut.pad(cut.duration + gap).append(shortest)
  74                 cuts = cuts[:-1]
  75                 break
  76             current_idx += 1
  77         if not can_fit:
  78             break
  79     return CutSet.from_cuts(cuts)
  80
  [end of lhotse/dataset/cut_transforms/concatenate.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/lhotse/dataset/cut_transforms/concatenate.py b/lhotse/dataset/cut_transforms/concatenate.py
  --- a/lhotse/dataset/cut_transforms/concatenate.py
  +++ b/lhotse/dataset/cut_transforms/concatenate.py
  @@ -62,7 +62,7 @@
           return CutSet.from_cuts(cuts)
       cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)
       max_duration = cuts[0].duration if max_duration is None else max_duration
  -    current_idx = 1
  +    current_idx = 0
       while True:
           can_fit = False
           shortest = cuts[-1]
  
verification_info: {"golden_diff": "diff --git a/lhotse/dataset/cut_transforms/concatenate.py b/lhotse/dataset/cut_transforms/concatenate.py\n--- a/lhotse/dataset/cut_transforms/concatenate.py\n+++ b/lhotse/dataset/cut_transforms/concatenate.py\n@@ -62,7 +62,7 @@\n         return CutSet.from_cuts(cuts)\n     cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)\n     max_duration = cuts[0].duration if max_duration is None else max_duration\n-    current_idx = 1\n+    current_idx = 0\n     while True:\n         can_fit = False\n         shortest = cuts[-1]\n", "issue": "Cut concatenate doesn't consider the first sample in each batch\nFound in #234 \n", "before_files": [{"content": "from typing import Optional, Sequence\n\nfrom lhotse import CutSet\nfrom lhotse.cut import AnyCut\nfrom lhotse.utils import Seconds\n\n\nclass CutConcatenate:\n    \"\"\"\n    A transform on batch of cuts (``CutSet``) that concatenates the cuts to minimize the total amount of padding;\n    e.g. instead of creating a batch with 40 examples, we will merge some of the examples together\n    adding some silence between them to avoid a large number of padding frames that waste the computation.\n    \"\"\"\n\n    def __init__(\n            self,\n            gap: Seconds = 1.0,\n            duration_factor: float = 1.0\n    ) -> None:\n        \"\"\"\n        CutConcatenate's constructor.\n\n        :param gap: The duration of silence in seconds that is inserted between the cuts;\n            it's goal is to let the model \"know\" that there are separate utterances in a single example.\n        :param duration_factor: Determines the maximum duration of the concatenated cuts;\n            by default it's 1, setting the limit at the duration of the longest cut in the batch.\n        \"\"\"\n        self.gap = gap\n        self.duration_factor = duration_factor\n\n    def __call__(self, cuts: CutSet) -> CutSet:\n        cuts = cuts.sort_by_duration(ascending=False)\n        return concat_cuts(\n            cuts,\n            gap=self.gap,\n            max_duration=cuts[0].duration * self.duration_factor\n        )\n\n\ndef concat_cuts(\n        cuts: Sequence[AnyCut],\n        gap: Seconds = 1.0,\n        max_duration: Optional[Seconds] = None\n) -> CutSet:\n    \"\"\"\n    We're going to concatenate the cuts to minimize the amount of total padding frames used.\n    This means that some samples in the batch will be merged together into one sample,\n    separated by an interval of silence.\n    This is actually solving a knapsack problem.\n    In this initial implementation we're using a greedy approach:\n    going from the back (i.e. the shortest cuts) we'll try to concat them to the longest cut\n    that still has some \"space\" at the end.\n\n    :param cuts: a list of cuts to pack.\n    :param gap: the duration of silence inserted between concatenated cuts.\n    :param max_duration: the maximum duration for the concatenated cuts\n        (by default set to the duration of the first cut).\n    :return a list of packed cuts.\n    \"\"\"\n    if len(cuts) <= 1:\n        # Nothing to do.\n        return CutSet.from_cuts(cuts)\n    cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)\n    max_duration = cuts[0].duration if max_duration is None else max_duration\n    current_idx = 1\n    while True:\n        can_fit = False\n        shortest = cuts[-1]\n        for idx in range(current_idx, len(cuts) - 1):\n            cut = cuts[current_idx]\n            can_fit = cut.duration + gap + shortest.duration <= max_duration\n            if can_fit:\n                cuts[current_idx] = cut.pad(cut.duration + gap).append(shortest)\n                cuts = cuts[:-1]\n                break\n            current_idx += 1\n        if not can_fit:\n            break\n    return CutSet.from_cuts(cuts)\n", "path": "lhotse/dataset/cut_transforms/concatenate.py"}]}