'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase_     		= '''scheduler_config.json'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							1
		lowercase   :      Any       =							2
		lowercase   :      List[str]       =							3
		lowercase   :      Dict       =							4
		lowercase   :      str       =							5

@dataclass
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      jnp.ndarray

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      Tuple       =							SCHEDULER_CONFIG_NAME
		lowercase   :      List[Any]       =							['dtype']
		lowercase   :      str       =							[]
		lowercase   :      Union[str, Any]       =							True






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A = None		,						__A = None		,						__A=False		,						**__A		,						) ->     Optional[int]:
								_lowerCAmelCase						,				_lowerCAmelCase										=cls.load_config(
								    pretrained_model_name_or_path=__A		,						subfolder=__A		,						return_unused_kwargs=__A		,						**__A		,						)
								_lowerCAmelCase						,				_lowerCAmelCase										=cls.from_config(__A		,						return_unused_kwargs=__A		,						**__A				)

								if hasattr(__A		,						'create_state'				) and getattr(__A		,						'has_state'		,						__A				):
														_lowerCAmelCase										=scheduler.create_state()

								if return_unused_kwargs:
														return scheduler, state, unused_kwargs

								return scheduler, state






		def 		UpperCamelCase__       (   self		,						__A		,						__A = False		,						**__A				) ->     Union[str, Any]:
								self.save_config(save_directory=__A		,						push_to_hub=__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Any:
								return self._get_compatibles()






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     Tuple:
								_lowerCAmelCase										=list(set([cls.__name__] + cls._compatibles				)				)
								_lowerCAmelCase										=importlib.import_module(__name__.split('.'				)[0]				)
								_lowerCAmelCase										=[
								    getattr(__A		,						__A				) for c in compatible_classes_str if hasattr(__A		,						__A				)
								]
								return compatible_classes



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						assert len(a__      ) >= x.ndim
						return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a__      ) - x.ndim)      )			,      a__      )



def 							UpperCamelCase__			(			a__			,      a__=0.999			,      a__=jnp.floataa      ):


						'''simple docstring'''






						def alpha_bar(a__      ):
												return math.cos((time_step + 0.008) / 1.008 * math.pi / 2      ) ** 2

						_lowerCAmelCase										=[]
						for i in range(a__      ):
												_lowerCAmelCase										=i / num_diffusion_timesteps
												_lowerCAmelCase										=(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar(a__      ) / alpha_bar(a__      )			,      a__      )      )
						return jnp.array(a__			,      dtype=a__      )
@flax.struct.dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      jnp.ndarray
		lowercase   :      jnp.ndarray
		lowercase   :      jnp.ndarray






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A				) ->     Dict:
								_lowerCAmelCase										=scheduler.config

								if config.trained_betas is not None:
														_lowerCAmelCase										=jnp.asarray(config.trained_betas		,						dtype=scheduler.dtype				)
								elif config.beta_schedule == "linear":
														_lowerCAmelCase										=jnp.linspace(config.beta_start		,						config.beta_end		,						config.num_train_timesteps		,						dtype=scheduler.dtype				)
								elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_lowerCAmelCase										=(
														    jnp.linspace(
														        config.beta_start**0.5		,						config.beta_end**0.5		,						config.num_train_timesteps		,						dtype=scheduler.dtype				)
														    ** 2
														)
								elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_lowerCAmelCase										=betas_for_alpha_bar(config.num_train_timesteps		,						dtype=scheduler.dtype				)
								else:
														raise NotImplementedError(
														    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''				)

								_lowerCAmelCase										=1.0 - betas

								_lowerCAmelCase										=jnp.cumprod(__A		,						axis=0				)

								return cls(
								    alphas=__A		,						betas=__A		,						alphas_cumprod=__A		,						)



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=state.alphas_cumprod

						_lowerCAmelCase										=alphas_cumprod[timesteps] ** 0.5
						_lowerCAmelCase										=sqrt_alpha_prod.flatten()
						_lowerCAmelCase										=broadcast_to_shape_from_left(a__			,      original_samples.shape      )

						_lowerCAmelCase										=(1 - alphas_cumprod[timesteps]) ** 0.5
						_lowerCAmelCase										=sqrt_one_minus_alpha_prod.flatten()
						_lowerCAmelCase										=broadcast_to_shape_from_left(a__			,      original_samples.shape      )

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase						,				_lowerCAmelCase										=get_sqrt_alpha_prod(a__			,      a__			,      a__			,      a__      )
						_lowerCAmelCase										=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase						,				_lowerCAmelCase										=get_sqrt_alpha_prod(a__			,      a__			,      a__			,      a__      )
						_lowerCAmelCase										=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity






'''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''': 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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     Optional[Any]:
								_lowerCAmelCase										=TOKEN
								HfFolder.save_token(__A				)






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     List[str]:
								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				) ->     str:
								_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 		UpperCamelCase__       (   self				) ->     Dict:
								_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 		UpperCamelCase__       (   self				) ->     List[str]:
								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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_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 		UpperCamelCase__       (   self				) ->     List[str]:
								_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 		UpperCamelCase__       (   self				) ->     Optional[int]:
								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 		UpperCamelCase__       (   self				) ->     List[str]:
								# A mock response for an HTTP head request to emulate server down
								_lowerCAmelCase										=mock.Mock()
								_lowerCAmelCase										=500
								_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 		UpperCamelCase__       (   self				) ->     Optional[int]:
								# This test is for deprecated behavior and can be removed in v5
								_lowerCAmelCase										=BertConfig.from_pretrained(
								    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_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										=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'				)				)
														_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
														self.assertEqual(new_configuration.hidden_size		,						768				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								_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		,						768				)





