from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class   lowerCAmelCase    :



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





     def __str__(       self							):
            return f"{self.data}"





class   lowerCAmelCase    :



     def __init__(       self							):
            _A=						None
            _A=						None



     def __iter__(       self							):
            _A=						self.front
            while node:
                   yield node.data
                   _A=						node.next



     def __len__(       self							):
            return len(tuple(iter(self							)							)							)



     def __str__(       self							):
            return " <- ".join(str(lowerCAmelCase__							) for item in self							)



     def       a__	(       self							):
            return len(self							) == 0



     def       a__	(       self					,   lowerCAmelCase__							):
            _A=						Node(lowerCAmelCase__							)
            if self.is_empty():
                   _A=						_A=						node
            else:
                   assert isinstance(self.rear					,   lowerCAmelCase__							)
                   _A=						node
                   _A=						node



     def       a__	(       self							):
            if self.is_empty():
                   raise IndexError('dequeue from empty queue'							)
            assert isinstance(self.front					,   lowerCAmelCase__							)
            _A=						self.front
            _A=						self.front.next
            if self.front is None:
                   _A=						None
            return node.data





     def       a__	(       self							):
            _A=						_A=						None


if __name__ == "__main__":
      from doctest import testmod

      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'							)							)