48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
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from __future__ import annotations
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from virtualenv.discovery.py_info import PythonInfo
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from virtualenv.discovery.py_spec import PythonSpec
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from .pep514 import discover_pythons
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# Map of well-known organizations (as per PEP 514 Company Windows Registry key part) versus Python implementation
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_IMPLEMENTATION_BY_ORG = {
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"ContinuumAnalytics": "CPython",
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"PythonCore": "CPython",
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}
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class Pep514PythonInfo(PythonInfo):
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"""A Python information acquired from PEP-514."""
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def propose_interpreters(spec, cache_dir, env):
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# see if PEP-514 entries are good
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# start with higher python versions in an effort to use the latest version available
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# and prefer PythonCore over conda pythons (as virtualenv is mostly used by non conda tools)
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existing = list(discover_pythons())
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existing.sort(
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key=lambda i: (*tuple(-1 if j is None else j for j in i[1:4]), 1 if i[0] == "PythonCore" else 0),
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reverse=True,
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)
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for name, major, minor, arch, exe, _ in existing:
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# Map well-known/most common organizations to a Python implementation, use the org name as a fallback for
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# backwards compatibility.
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implementation = _IMPLEMENTATION_BY_ORG.get(name, name)
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# Pre-filtering based on Windows Registry metadata, for CPython only
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skip_pre_filter = implementation.lower() != "cpython"
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registry_spec = PythonSpec(None, implementation, major, minor, None, arch, exe)
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if skip_pre_filter or registry_spec.satisfies(spec):
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interpreter = Pep514PythonInfo.from_exe(exe, cache_dir, env=env, raise_on_error=False)
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if interpreter is not None and interpreter.satisfies(spec, impl_must_match=True):
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yield interpreter # Final filtering/matching using interpreter metadata
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__all__ = [
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"Pep514PythonInfo",
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"propose_interpreters",
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]
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