from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

UpperCAmelCase_    		=				TypeVar('''T''', bound=bool)





class   lowerCAmelCase    (       Generic[T]      ):



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						value
            _A=						None
            _A=						None



     @property
     def       a__	(       self							):
            return self._value





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   lowerCAmelCase__							):
            if not roota:
                   return roota

            if not roota:
                   return roota

            if roota.value > roota.value:
                   _A,      _A=						roota, roota

            _A=						roota
            _A=						roota.right
            _A=						roota.left
            _A=						SkewNode.merge(lowerCAmelCase__					,   lowerCAmelCase__							)

            return result





class   lowerCAmelCase    (       Generic[T]      ):



     def __init__(       self					,   lowerCAmelCase__ = ()							):
            _A=						None
            if data:
                   for item in data:
                          self.insert(lowerCAmelCase__							)



     def __bool__(       self							):
            return self._root is not None



     def __iter__(       self							):
            _A=						[]
            while self:
                   result.append(self.pop()							)

            # Pushing items back to the heap not to clear it.
            for item in result:
                   self.insert(lowerCAmelCase__							)

            return iter(lowerCAmelCase__							)



     def       a__	(       self					,   lowerCAmelCase__							):
            _A=						SkewNode.merge(self._root					,   SkewNode(lowerCAmelCase__							)							)



     def       a__	(       self							):
            _A=						self.top()
            _A=						(
                SkewNode.merge(self._root.left					,   self._root.right							) if self._root else None
            )

            return result



     def       a__	(       self							):
            if not self._root:
                   raise IndexError('Can\'t get top element for the empty heap.'							)
            return self._root.value





     def       a__	(       self							):
            _A=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch





class   lowerCAmelCase    (       unittest.TestCase      ):



     @patch('sys.argv'					,   ['fakeprogrampath', 'env']							)
     def       a__	(       self							):
            # test transformers-cli env
            import transformers.commands.transformers_cli

            with CaptureStd() as cs:
                   transformers.commands.transformers_cli.main()
            self.assertIn('Python version'					,   cs.out							)
            self.assertIn('Platform'					,   cs.out							)
            self.assertIn('Using distributed or parallel set-up in script?'					,   cs.out							)



     @is_pt_tf_cross_test
     @patch(
         'sys.argv'					,   ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)  # cleans potential past runs
            transformers.commands.transformers_cli.main()

            # The original repo has no TF weights -- if they exist, they were created by the CLI
            self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'							)							)



     @require_torch
     @patch('sys.argv'					,   ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'							)							)





     @require_torch
     @patch(
         'sys.argv'					,   [
             'fakeprogrampath',
             'download',
             'hf-internal-testing/test_dynamic_model_with_tokenizer',
             '--trust-remote-code',
             '--cache-dir',
             '/tmp',
         ]					,   )
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'							)							)
            self.assertTrue(
                os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'							)							)