a Sic @s<dZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlZddlZddlZddlmZddlmZddlZddlmmZddlmZddlmZddlmZddl m!Z!dd l"m#Z#dd l$m%Z%dId d Z&d dZ'dJddZ(e#ddKddZ)ddZ*dLddZ+dMddZ,dNd d!Z-Gd"d#d#Z.d$d%Z/e#d&Gd'd(d(Z0d)d*Z1ia2da3e4Z5da6e7Z8da9e :Z;d+d,Zd1d2Z?d3d4Z@e#d5Gd6d7d7ZAe#d8Gd9d:d:eAZBdOd;d<ZCd=d>ZDe#d?Gd@dAdAeAZEe#dBdCdPdGdHZFdS)Qz(Utilities for file download and caching.N)abstractmethod)closing)urlsplit)io_utils) tf_inspect)Progbar) keras_export)urlopencCs^ddd}t||}t|d,}|||dD]}||q,Wdn1sP0YdS)aReplacement for `urlretrieve` for Python 2. Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy `urllib` module, known to have issues with proxy management. Args: url: url to retrieve. filename: where to store the retrieved data locally. reporthook: a hook function that will be called once on establishment of the network connection and once after each block read thereafter. The hook will be passed three arguments; a count of blocks transferred so far, a block size in bytes, and the total size of the file. data: `data` argument passed to `urlopen`. Ncssd|d}d}|dur&t|}d}||}|d7}|durP|||||r`|Vq*q`q*dS)NzContent-Lengthr)infogetintstripread)response chunk_size reporthook content_type total_sizecountchunkrR/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/utils/data_utils.py chunk_readDs   zurlretrieve..chunk_readwb)r)r N)r openwrite)urlfilenamerdatarrfdrrrr urlretrieve3s    r#cCsJttttttf}t|tjt j f|r*dSt |pHt|t pHt|tjS)z'Check if `x` is a Keras generator type.F)strlisttupledictset frozenset isinstancetfTensornpndarrayr isgeneratorSequencetypingIterator)xbuiltin_iteratorsrrris_generator_or_sequence]s  r5.autoc Cs|dur dS|dkrddg}t|tr,|g}t|}t|}|D]}|dkr\tj}tj}|dkrptj}tj }||rD||j}z| |WnJtj t t fytj|rtj|rt|n t|Yn0Wdn1s0YdSqDdS)aNExtracts an archive if it matches tar, tar.gz, tar.bz, or zip formats. 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NFr7tarzipT)r*r$rpath_to_stringtarfiler is_tarfilezipfileZipFile is_zipfile extractallTarError RuntimeErrorKeyboardInterruptospathexistsisfileremoveshutilrmtree) file_pathrEarchive_format archive_typeopen_fn is_match_fnarchiverrr_extract_archiveis6        &rQzkeras.utils.get_fileFdatasetsc  Cs|durtd| dur.tjtjdd} |durF|durF|}d}tj| } t| tjsntjdd} tj| |} t| t |}|stj t |j}|std|d|r| d rt |}|d d }t|}tj| |} | d } ntj| |} d }tj| rZ|dur^t| ||d s^td |d|dd}nd}|rtd|Gddd}d}zzt|| |Wn~tjjy}z"t|||j|jWYd}~nHd}~0tjjy}z"t|||j|jWYd}~n d}~00Wn2ttfyNtj| rHt | Yn0tj| r|durt| ||d std|d|d|rtj| st!| | dd| S|rt!| | || S)aM Downloads a file from a URL if it not already in the cache. By default the file at the url `origin` is downloaded to the cache_dir `~/.keras`, placed in the cache_subdir `datasets`, and given the filename `fname`. The final location of a file `example.txt` would therefore be `~/.keras/datasets/example.txt`. Files in tar, tar.gz, tar.bz, and zip formats can also be extracted. Passing a hash will verify the file after download. The command line programs `shasum` and `sha256sum` can compute the hash. Example: ```python path_to_downloaded_file = tf.keras.utils.get_file( "flower_photos", "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", untar=True) ``` Args: fname: Name of the file. If an absolute path `/path/to/file.txt` is specified the file will be saved at that location. If `None`, the name of the file at `origin` will be used. origin: Original URL of the file. untar: Deprecated in favor of `extract` argument. boolean, whether the file should be decompressed md5_hash: Deprecated in favor of `file_hash` argument. md5 hash of the file for verification file_hash: The expected hash string of the file after download. The sha256 and md5 hash algorithms are both supported. cache_subdir: Subdirectory under the Keras cache dir where the file is saved. 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Sequences longer than `num_timesteps` are truncated so that they fit the desired length. The position where padding or truncation happens is determined by the arguments `padding` and `truncating`, respectively. Pre-padding or removing values from the beginning of the sequence is the default. >>> sequence = [[1], [2, 3], [4, 5, 6]] >>> tf.keras.preprocessing.sequence.pad_sequences(sequence) array([[0, 0, 1], [0, 2, 3], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, value=-1) array([[-1, -1, 1], [-1, 2, 3], [ 4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post') array([[1, 0, 0], [2, 3, 0], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2) array([[0, 1], [2, 3], [5, 6]], dtype=int32) Args: sequences: List of sequences (each sequence is a list of integers). maxlen: Optional Int, maximum length of all sequences. 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