




"""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


__A					=      {
    "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": 128,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 50,
    "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": 10,
    "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": 10,
    "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      ):


			@classmethod
			def 				lowerCamelCase_					(  cls				):



					"""simple docstring"""







					snake_case		:    str             =					TOKEN
					HfFolder.save_token(SCREAMING_SNAKE_CASE				)


			@classmethod
			def 				lowerCamelCase_					(  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 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Dict             =					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				)

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

        # 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(SCREAMING_SNAKE_CASE					,			repo_id="test-config"					,			push_to_hub=SCREAMING_SNAKE_CASE					,			use_auth_token=self._token				)

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


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    List[Any]             =					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				)

					snake_case		:    Optional[int]             =					BertConfig.from_pretrained("valid_org/test-config-org"				)
					for k, v in config.to_dict().items():
							if k != "transformers_version":
									self.assertEqual(SCREAMING_SNAKE_CASE					,			getattr(SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE				)				)

        # 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(
							    SCREAMING_SNAKE_CASE					,			repo_id="valid_org/test-config-org"					,			push_to_hub=SCREAMING_SNAKE_CASE					,			use_auth_token=self._token				)

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



			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					CustomConfig.register_for_auto_class()
					snake_case		:    Dict             =					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"}				)

					snake_case		:    Optional[Any]             =					AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''					,			trust_remote_code=SCREAMING_SNAKE_CASE				)
					# 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      ):


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    int             =					GPTaConfig()

					# attempt to modify each of int/float/bool/str config records and verify they were updated
					snake_case		:    int             =					c.n_embd + 1  # int
					snake_case		:    List[Any]             =					c.resid_pdrop + 1.0  # float
					snake_case		:    str             =					not c.scale_attn_weights  # bool
					snake_case		:    Dict             =					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(SCREAMING_SNAKE_CASE					,			c.n_embd					,			"mismatch for key: n_embd"				)
					self.assertEqual(SCREAMING_SNAKE_CASE					,			c.resid_pdrop					,			"mismatch for key: resid_pdrop"				)
					self.assertEqual(SCREAMING_SNAKE_CASE					,			c.scale_attn_weights					,			"mismatch for key: scale_attn_weights"				)
					self.assertEqual(SCREAMING_SNAKE_CASE					,			c.summary_type					,			"mismatch for key: summary_type"				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Union[str, Any]             =					PretrainedConfig()
					snake_case		:    Dict             =					[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(
					    SCREAMING_SNAKE_CASE					,			["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]				)
					snake_case		:    List[str]             =					[key for key, value in config_common_kwargs.items() if value == getattr(SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE				)]
					if len(SCREAMING_SNAKE_CASE				) > 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(SCREAMING_SNAKE_CASE				)}.'''				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







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

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

					self.assertIsNotNone(SCREAMING_SNAKE_CASE				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    str             =					mock.Mock()
					snake_case		:    List[str]             =					500
					snake_case		:    Union[str, Any]             =					{}
					snake_case		:    Tuple             =					HTTPError
					snake_case		:    Dict             =					{}

					# Download this model to make sure it's in the cache.
					snake_case		:    int             =					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=SCREAMING_SNAKE_CASE				) as mock_head:
							snake_case		:    List[Any]             =					BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"				)
							# This check we did call the fake head request
							mock_head.assert_called()


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    List[Any]             =					BertConfig.from_pretrained(
					    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Dict             =					AutoConfig.from_pretrained("bert-base-cased"				)
					snake_case		:    List[Any]             =					["config.4.0.0.json"]

					with tempfile.TemporaryDirectory() as tmp_dir:
							configuration.save_pretrained(SCREAMING_SNAKE_CASE				)
							snake_case		:    Optional[Any]             =					2
							json.dump(configuration.to_dict()					,			open(os.path.join(SCREAMING_SNAKE_CASE					,			"config.4.0.0.json"				)					,			"w"				)				)

							# This should pick the new configuration file as the version of Transformers is > 4.0.0
							snake_case		:    Any             =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
							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
							snake_case		:    Tuple             =					["config.42.0.0.json"]
							snake_case		:    Optional[int]             =					768
							configuration.save_pretrained(SCREAMING_SNAKE_CASE				)
							shutil.move(os.path.join(SCREAMING_SNAKE_CASE					,			"config.4.0.0.json"				)					,			os.path.join(SCREAMING_SNAKE_CASE					,			"config.42.0.0.json"				)				)
							snake_case		:    Any             =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
							self.assertEqual(new_configuration.hidden_size					,			768				)



			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    List[Any]             =					"hf-internal-testing/test-two-configs"

					import transformers as new_transformers

					snake_case		:    str             =					"v4.0.0"
					snake_case      ,					snake_case		:    Optional[Any]             =					new_transformers.models.auto.AutoConfig.from_pretrained(
					    SCREAMING_SNAKE_CASE					,			return_unused_kwargs=SCREAMING_SNAKE_CASE				)
					self.assertEqual(new_configuration.hidden_size					,			2				)
					# This checks `_configuration_file` ia not kept in the kwargs by mistake.
					self.assertDictEqual(SCREAMING_SNAKE_CASE					,			{}				)

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

					snake_case		:    List[Any]             =					"v3.0.0"
					snake_case		:    str             =					old_transformers.models.auto.AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
					self.assertEqual(old_configuration.hidden_size					,			768				)



'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
