--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/tqdm/keras.py	Tue Mar 18 16:23:26 2025 -0400
@@ -0,0 +1,122 @@
+from copy import copy
+from functools import partial
+
+from .auto import tqdm as tqdm_auto
+
+try:
+    import keras
+except (ImportError, AttributeError) as e:
+    try:
+        from tensorflow import keras
+    except ImportError:
+        raise e
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['TqdmCallback']
+
+
+class TqdmCallback(keras.callbacks.Callback):
+    """Keras callback for epoch and batch progress."""
+    @staticmethod
+    def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
+        def callback(_, logs=None):
+            n = delta(logs)
+            if logs:
+                if pop:
+                    logs = copy(logs)
+                    [logs.pop(i, 0) for i in pop]
+                bar.set_postfix(logs, refresh=False)
+            bar.update(n)
+
+        return callback
+
+    def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
+                 tqdm_class=tqdm_auto, **tqdm_kwargs):
+        """
+        Parameters
+        ----------
+        epochs  : int, optional
+        data_size  : int, optional
+            Number of training pairs.
+        batch_size  : int, optional
+            Number of training pairs per batch.
+        verbose  : int
+            0: epoch, 1: batch (transient), 2: batch. [default: 1].
+            Will be set to `0` unless both `data_size` and `batch_size`
+            are given.
+        tqdm_class  : optional
+            `tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
+        tqdm_kwargs  : optional
+            Any other arguments used for all bars.
+        """
+        if tqdm_kwargs:
+            tqdm_class = partial(tqdm_class, **tqdm_kwargs)
+        self.tqdm_class = tqdm_class
+        self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
+        self.on_epoch_end = self.bar2callback(self.epoch_bar)
+        if data_size and batch_size:
+            self.batches = batches = (data_size + batch_size - 1) // batch_size
+        else:
+            self.batches = batches = None
+        self.verbose = verbose
+        if verbose == 1:
+            self.batch_bar = tqdm_class(total=batches, unit='batch', leave=False)
+            self.on_batch_end = self.bar2callback(
+                self.batch_bar, pop=['batch', 'size'],
+                delta=lambda logs: logs.get('size', 1))
+
+    def on_train_begin(self, *_, **__):
+        params = self.params.get
+        auto_total = params('epochs', params('nb_epoch', None))
+        if auto_total is not None and auto_total != self.epoch_bar.total:
+            self.epoch_bar.reset(total=auto_total)
+
+    def on_epoch_begin(self, epoch, *_, **__):
+        if self.epoch_bar.n < epoch:
+            ebar = self.epoch_bar
+            ebar.n = ebar.last_print_n = ebar.initial = epoch
+        if self.verbose:
+            params = self.params.get
+            total = params('samples', params(
+                'nb_sample', params('steps', None))) or self.batches
+            if self.verbose == 2:
+                if hasattr(self, 'batch_bar'):
+                    self.batch_bar.close()
+                self.batch_bar = self.tqdm_class(
+                    total=total, unit='batch', leave=True,
+                    unit_scale=1 / (params('batch_size', 1) or 1))
+                self.on_batch_end = self.bar2callback(
+                    self.batch_bar, pop=['batch', 'size'],
+                    delta=lambda logs: logs.get('size', 1))
+            elif self.verbose == 1:
+                self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
+                self.batch_bar.reset(total=total)
+            else:
+                raise KeyError('Unknown verbosity')
+
+    def on_train_end(self, *_, **__):
+        if hasattr(self, 'batch_bar'):
+            self.batch_bar.close()
+        self.epoch_bar.close()
+
+    def display(self):
+        """Displays in the current cell in Notebooks."""
+        container = getattr(self.epoch_bar, 'container', None)
+        if container is None:
+            return
+        from .notebook import display
+        display(container)
+        batch_bar = getattr(self, 'batch_bar', None)
+        if batch_bar is not None:
+            display(batch_bar.container)
+
+    @staticmethod
+    def _implements_train_batch_hooks():
+        return True
+
+    @staticmethod
+    def _implements_test_batch_hooks():
+        return True
+
+    @staticmethod
+    def _implements_predict_batch_hooks():
+        return True