



import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


a_						:     Tuple	     =		{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_2_8,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 5_0,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 1_0,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 1_0,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @classmethod
     def 						_a      (cls    ):






           '''simple docstring'''



           lowerCamelCase								=		TOKEN
           HfFolder.save_token(__a    )







     @classmethod
     def 						_a      (cls    ):






           '''simple docstring'''



           try:
                 delete_repo(token=cls._token				,			repo_id="test-config"    )
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token				,			repo_id="valid_org/test-config-org"    )
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token				,			repo_id="test-dynamic-config"    )
           except HTTPError:
                 pass







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BertConfig(
               vocab_size=99				,			hidden_size=32				,			num_hidden_layers=5				,			num_attention_heads=4				,			intermediate_size=37    )
           config.push_to_hub("test-config"				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertConfig.from_pretrained(F"""{USER}/test-config"""    )
           for k, v in config.to_dict().items():
                 if k != "transformers_version":
                       self.assertEqual(__a				,			getattr(__a				,			__a    )    )

        # Reset repo
           delete_repo(token=self._token				,			repo_id="test-config"    )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(__a				,			repo_id="test-config"				,			push_to_hub=__a				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertConfig.from_pretrained(F"""{USER}/test-config"""    )
           for k, v in config.to_dict().items():
                 if k != "transformers_version":
                       self.assertEqual(__a				,			getattr(__a				,			__a    )    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BertConfig(
               vocab_size=99				,			hidden_size=32				,			num_hidden_layers=5				,			num_attention_heads=4				,			intermediate_size=37    )
           config.push_to_hub("valid_org/test-config-org"				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertConfig.from_pretrained("valid_org/test-config-org"    )
           for k, v in config.to_dict().items():
                 if k != "transformers_version":
                       self.assertEqual(__a				,			getattr(__a				,			__a    )    )

        # Reset repo
           delete_repo(token=self._token				,			repo_id="valid_org/test-config-org"    )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(
                     __a				,			repo_id="valid_org/test-config-org"				,			push_to_hub=__a				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertConfig.from_pretrained("valid_org/test-config-org"    )
           for k, v in config.to_dict().items():
                 if k != "transformers_version":
                       self.assertEqual(__a				,			getattr(__a				,			__a    )    )







     def 						_a      (self    ):






           '''simple docstring'''



           CustomConfig.register_for_auto_class()
           lowerCamelCase								=		CustomConfig(attribute=42    )

           config.push_to_hub("test-dynamic-config"				,			use_auth_token=self._token    )

           # This has added the proper auto_map field to the config
           self.assertDictEqual(config.auto_map				,			{"AutoConfig": "custom_configuration.CustomConfig"}    )

           lowerCamelCase								=		AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config"""				,			trust_remote_code=__a    )
           # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
           self.assertEqual(new_config.__class__.__name__				,			"CustomConfig"    )
           self.assertEqual(new_config.attribute				,			42    )





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		GPTaConfig()

           # attempt to modify each of int/float/bool/str config records and verify they were updated
           lowerCamelCase								=		c.n_embd + 1  # int
           lowerCamelCase								=		c.resid_pdrop + 1.0  # float
           lowerCamelCase								=		not c.scale_attn_weights  # bool
           lowerCamelCase								=		c.summary_type + "foo"  # str
           c.update_from_string(
               F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""    )
           self.assertEqual(__a				,			c.n_embd				,			"mismatch for key: n_embd"    )
           self.assertEqual(__a				,			c.resid_pdrop				,			"mismatch for key: resid_pdrop"    )
           self.assertEqual(__a				,			c.scale_attn_weights				,			"mismatch for key: scale_attn_weights"    )
           self.assertEqual(__a				,			c.summary_type				,			"mismatch for key: summary_type"    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		PretrainedConfig()
           lowerCamelCase								=		[key for key in base_config.__dict__ if key not in config_common_kwargs]
           # If this part of the test fails, you have arguments to addin config_common_kwargs above.
           self.assertListEqual(
               __a				,			["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]    )
           lowerCamelCase								=		[key for key, value in config_common_kwargs.items() if value == getattr(__a				,			__a    )]
           if len(__a    ) > 0:
                 raise ValueError(
                     "The following keys are set with the default values in"
                     " `test_configuration_common.config_common_kwargs` pick another value for them:"
                     F""" {", ".join(__a    )}."""    )







     def 						_a      (self    ):






           '''simple docstring'''



           with self.assertRaises(__a    ):
                 # config is in subfolder, the following should not work without specifying the subfolder
                 lowerCamelCase								=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"    )

           lowerCamelCase								=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"				,			subfolder="bert"    )

           self.assertIsNotNone(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		mock.Mock()
           lowerCamelCase								=		5_00
           lowerCamelCase								=		{}
           lowerCamelCase								=		HTTPError
           lowerCamelCase								=		{}

           # Download this model to make sure it's in the cache.
           lowerCamelCase								=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"    )

           # Under the mock environment we get a 500 error when trying to reach the model.
           with mock.patch("requests.Session.request"				,			return_value=__a    ) as mock_head:
                 lowerCamelCase								=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"    )
                 # This check we did call the fake head request
                 mock_head.assert_called()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BertConfig.from_pretrained(
               "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		AutoConfig.from_pretrained("bert-base-cased"    )
           lowerCamelCase								=		["config.4.0.0.json"]

           with tempfile.TemporaryDirectory() as tmp_dir:
                 configuration.save_pretrained(__a    )
                 lowerCamelCase								=		2
                 json.dump(configuration.to_dict()				,			open(os.path.join(__a				,			"config.4.0.0.json"    )				,			"w"    )    )

                 # This should pick the new configuration file as the version of Transformers is > 4.0.0
                 lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
                 self.assertEqual(new_configuration.hidden_size				,			2    )

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old configuration file as the version of Transformers is < 4.42.0
                 lowerCamelCase								=		["config.42.0.0.json"]
                 lowerCamelCase								=		7_68
                 configuration.save_pretrained(__a    )
                 shutil.move(os.path.join(__a				,			"config.4.0.0.json"    )				,			os.path.join(__a				,			"config.42.0.0.json"    )    )
                 lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
                 self.assertEqual(new_configuration.hidden_size				,			7_68    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"hf-internal-testing/test-two-configs"

           import transformers as new_transformers

           lowerCamelCase								=		"v4.0.0"
           lowerCamelCase			,							lowerCamelCase								=		new_transformers.models.auto.AutoConfig.from_pretrained(
               __a				,			return_unused_kwargs=__a    )
           self.assertEqual(new_configuration.hidden_size				,			2    )
           # This checks `_configuration_file` ia not kept in the kwargs by mistake.
           self.assertDictEqual(__a				,			{}    )

           # Testing an older version by monkey-patching the version in the module it's used.
           import transformers as old_transformers

           lowerCamelCase								=		"v3.0.0"
           lowerCamelCase								=		old_transformers.models.auto.AutoConfig.from_pretrained(__a    )
           self.assertEqual(old_configuration.hidden_size				,			7_68    )




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if weight not in (0, 1):
                 raise ValueError("Edge weight must be either 0 or 1."    )

           if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("Vertex indexes must be in [0; size)."    )

           self._graph[from_vertex].append(Edge(__a				,			__a    )    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

           while queue:
                 lowerCamelCase								=		queue.popleft()
                 lowerCamelCase								=		distances[current_vertex]
                 if current_distance is None:
                       continue

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		new_distance
                       if edge.weight == 0:
                             queue.appendleft(edge.destination_vertex    )
                       else:
                             queue.append(edge.destination_vertex    )

           if distances[finish_vertex] is None:
                 raise ValueError("No path from start_vertex to finish_vertex."    )

           return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()