


'''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


lowerCAmelCase     :  Union[str, Any]											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self   ,						snake_case__=32 ):

            '''simple docstring'''

            set_seed(0 )
            _lowerCAmelCase     :				Tuple       =							UNetaDModel(sample_size=snake_case__   ,						in_channels=3   ,						out_channels=3 )
            _lowerCAmelCase     :				int       =							torch.optim.SGD(model.parameters()   ,						lr=0.0001 )
            return model, optimizer







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
            _lowerCAmelCase     :				Tuple       =							DDPMScheduler(
                num_train_timesteps=1000   ,						beta_start=0.0001   ,						beta_end=0.02   ,						beta_schedule='linear'   ,						clip_sample=snake_case__   ,						)
            _lowerCAmelCase     :				Union[str, Any]       =							DDIMScheduler(
                num_train_timesteps=1000   ,						beta_start=0.0001   ,						beta_end=0.02   ,						beta_schedule='linear'   ,						clip_sample=snake_case__   ,						)

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

            # shared batches for DDPM and DDIM
            set_seed(0 )
            _lowerCAmelCase     :				int       =							[torch.randn((4, 3, 32, 32) ).clip(-1   ,						1 ).to(snake_case__ ) for _ in range(4 )]
            _lowerCAmelCase     :				Optional[int]       =							[torch.randn((4, 3, 32, 32) ).to(snake_case__ ) for _ in range(4 )]
            _lowerCAmelCase     :				List[Any]       =							[torch.randint(0   ,						1000   ,						(4,) ).long().to(snake_case__ ) for _ in range(4 )]

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

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

            self.assertTrue(torch.allclose(snake_case__   ,						snake_case__   ,						atol=1E-5 ) )
            self.assertTrue(torch.allclose(snake_case__   ,						snake_case__   ,						atol=1E-5 ) )





'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

