


"""simple docstring"""




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


lowercase_     	=    {
    "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      __UpperCAmelCase			(    cls ):
				__a	 =     TOKEN
				HfFolder.save_token(A_ )

		@classmethod
		def      __UpperCAmelCase			(    cls ):
				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      __UpperCAmelCase			(    self ):
				__a	 =     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 )

				__a	 =     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 )

				__a	 =     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      __UpperCAmelCase			(    self ):
				__a	 =     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 )

				__a	 =     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 )

				__a	 =     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      __UpperCAmelCase			(    self ):
				CustomConfig.register_for_auto_class()
				__a	 =     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'''} )

				__a	 =     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      __UpperCAmelCase			(    self ):
				__a	 =     GPTaConfig()

				# attempt to modify each of int/float/bool/str config records and verify they were updated
				__a	 =     c.n_embd + 1  # int
				__a	 =     c.resid_pdrop + 1.0  # float
				__a	 =     not c.scale_attn_weights  # bool
				__a	 =     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      __UpperCAmelCase			(    self ):
				__a	 =     PretrainedConfig()
				__a	 =     [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'''] )
				__a	 =     [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      __UpperCAmelCase			(    self ):
				with self.assertRaises(A_ ):
						# config is in subfolder, the following should not work without specifying the subfolder
						__a	 =     BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' )

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

				self.assertIsNotNone(A_ )

		def      __UpperCAmelCase			(    self ):
				# A mock response for an HTTP head request to emulate server down
				__a	 =     mock.Mock()
				__a	 =     500
				__a	 =     {}
				__a	 =     HTTPError
				__a	 =     {}

				# Download this model to make sure it's in the cache.
				__a	 =     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:
						__a	 =     BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
						# This check we did call the fake head request
						mock_head.assert_called()

		def      __UpperCAmelCase			(    self ):
				# This test is for deprecated behavior and can be removed in v5
				__a	 =     BertConfig.from_pretrained(
				    '''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json''' )

		def      __UpperCAmelCase			(    self ):
				__a	 =     AutoConfig.from_pretrained('''bert-base-cased''' )
				__a	 =     ['''config.4.0.0.json''']

				with tempfile.TemporaryDirectory() as tmp_dir:
						configuration.save_pretrained(A_ )
						__a	 =     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
						__a	 =     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
						__a	 =     ['''config.42.0.0.json''']
						__a	 =     768
						configuration.save_pretrained(A_ )
						shutil.move(os.path.join(A_				,					'''config.4.0.0.json''' )				,					os.path.join(A_				,					'''config.42.0.0.json''' ) )
						__a	 =     AutoConfig.from_pretrained(A_ )
						self.assertEqual(new_configuration.hidden_size				,					768 )





		def      __UpperCAmelCase			(    self ):
				# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
				__a	 =     '''hf-internal-testing/test-two-configs'''

				import transformers as new_transformers

				__a	 =     '''v4.0.0'''
				__a ,	__a	 =     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

				__a	 =     '''v3.0.0'''
				__a	 =     old_transformers.models.auto.AutoConfig.from_pretrained(A_ )
				self.assertEqual(old_configuration.hidden_size				,					768 )








"""simple docstring"""


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

							A     = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
							A     = dict(zip(A_	,range(len(A_   )   )   )   )

							A     = ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
							A     = {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write(json.dumps(A_   ) + '\n'   )
							with open(self.merges_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A     = [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = tok.batch_decode(A_	,skip_special_tokens=A_	,clean_up_tokenization_spaces=A_   )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

							assert encoded[-1] == encoded_dot[0]