

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_            = {
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE_            = {
    'Salesforce/codegen-350M-mono': 2048,
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase        =     ["input_ids", "attention_mask"]
						_lowercase        =     CodeGenTokenizer


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







										'''simple docstring'''


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

										if kwargs.pop("add_bos_token"  ,   A_		):
														_UpperCAmelCase    :						List[Any]							=      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."		)

										_UpperCAmelCase    :						Union[str, Any]							=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
										if pre_tok_state.get("add_prefix_space"  ,   A_		) != add_prefix_space:
														_UpperCAmelCase    :						List[str]							=      getattr(A_  ,   pre_tok_state.pop("type"		)		)
														_UpperCAmelCase    :						List[str]							=      add_prefix_space
														_UpperCAmelCase    :						List[str]							=      pre_tok_class(**A_		)

										_UpperCAmelCase    :						Any							=      add_prefix_space


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      kwargs.get("is_split_into_words"  ,   A_		)
										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(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      kwargs.get("is_split_into_words"  ,   A_		)

										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(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self._tokenizer.model.save(A_  ,   name=A_		)
										return tuple(A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = False  ,   A_ = None  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      super().decode(
										    token_ids=A_  ,   skip_special_tokens=A_  ,   clean_up_tokenization_spaces=A_  ,   **A_  ,   )

										if truncate_before_pattern is not None and len(A_		) > 0:
														_UpperCAmelCase    :						Dict							=      self.truncate(A_  ,   A_		)

										return decoded_text





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										def find_re(A_  ,   A_  ,   A_		):
														_UpperCAmelCase    :						str							=      pattern.search(A_  ,   A_		)
														return m.start() if m else -1

										_UpperCAmelCase    :						int							=      [re.compile(A_  ,   re.MULTILINE		) for pattern in truncate_before_pattern]

										_UpperCAmelCase    :						int							=      list(re.finditer("^print"  ,   A_  ,   re.MULTILINE		)		)

										if len(A_		) > 1:
														_UpperCAmelCase    :						Dict							=      completion[: prints[1].start()]

										_UpperCAmelCase    :						Dict							=      list(re.finditer("^def"  ,   A_  ,   re.MULTILINE		)		)

										if len(A_		) > 1:
														_UpperCAmelCase    :						Dict							=      completion[: defs[1].start()]

										_UpperCAmelCase    :						Tuple							=      0

										_UpperCAmelCase    :						int							=      [
										    pos for pos in [find_re(A_  ,   A_  ,   A_		) for terminal in terminals] if pos != -1
										]

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





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (PNDMScheduler,)
						_lowercase        =     (("num_inference_steps", 5_0),)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						Optional[Any]							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						Dict							=      0.1 * sample
										_UpperCAmelCase    :						Optional[int]							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						Any							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						str							=      scheduler_class.from_pretrained(A_		)
																		new_scheduler.set_timesteps(A_		)
																		# copy over dummy past residuals
																		_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Any							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_UpperCAmelCase    :						Union[str, Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						int							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						Any							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Optional[Any]							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Dict							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)

														# copy over dummy past residuals (must be after setting timesteps)
														_UpperCAmelCase    :						Any							=      dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						str							=      scheduler_class.from_pretrained(A_		)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(A_		)

																		# copy over dummy past residual (must be after setting timesteps)
																		_UpperCAmelCase    :						List[Any]							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Union[str, Any]							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						List[str]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Union[str, Any]							=      self.get_scheduler_config(**A_		)
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						Optional[int]							=      self.dummy_model()
										_UpperCAmelCase    :						List[Any]							=      self.dummy_sample_deter
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.prk_timesteps		):
														_UpperCAmelCase    :						Dict							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						int							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps		):
														_UpperCAmelCase    :						Any							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						Any							=      scheduler.step_plms(A_  ,   A_  ,   A_		).prev_sample

										return sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      dict(self.forward_default_kwargs		)

										_UpperCAmelCase    :						str							=      kwargs.pop("num_inference_steps"  ,   A_		)

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						int							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Optional[Any]							=      scheduler_class(**A_		)

														_UpperCAmelCase    :						int							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														if num_inference_steps is not None and hasattr(A_  ,   "set_timesteps"		):
																		scheduler.set_timesteps(A_		)
														elif num_inference_steps is not None and not hasattr(A_  ,   "set_timesteps"		):
																		_UpperCAmelCase    :						Union[str, Any]							=      num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														_UpperCAmelCase    :						Any							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Any							=      scheduler.step_prk(A_  ,   0  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_prk(A_  ,   1  ,   A_  ,   **A_		).prev_sample

														self.assertEqual(output_a.shape  ,   sample.shape		)
														self.assertEqual(output_a.shape  ,   output_a.shape		)

														_UpperCAmelCase    :						Optional[int]							=      scheduler.step_plms(A_  ,   0  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_plms(A_  ,   1  ,   A_  ,   **A_		).prev_sample

														self.assertEqual(output_a.shape  ,   sample.shape		)
														self.assertEqual(output_a.shape  ,   output_a.shape		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=A_		)

										_UpperCAmelCase    :						List[str]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(steps_offset=1		)
										_UpperCAmelCase    :						str							=      scheduler_class(**A_		)
										scheduler.set_timesteps(10		)
										assert torch.equal(
										    scheduler.timesteps  ,   torch.LongTensor(
										        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.00_01, 0.0_01]  ,   [0.0_02, 0.02]		):
														self.check_over_configs(beta_start=A_  ,   beta_end=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for t in [1, 5, 10]:
														self.check_over_forward(time_step=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for t, num_inference_steps in zip([1, 5, 10]  ,   [10, 50, 100]		):
														self.check_over_forward(num_inference_steps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      27

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						str							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														_UpperCAmelCase    :						str							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Tuple							=      scheduler_class(**A_		)

														scheduler.set_timesteps(A_		)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]		):
																		_UpperCAmelCase    :						Dict							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with self.assertRaises(A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      self.scheduler_classes[0]
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Union[str, Any]							=      scheduler_class(**A_		)

														scheduler.step_plms(self.dummy_sample  ,   1  ,   self.dummy_sample		).prev_sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.full_loop()
										_UpperCAmelCase    :						int							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 1_98.13_18		) < 1e-2
										assert abs(result_mean.item() - 0.25_80		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.full_loop(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						List[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 67.39_86		) < 1e-2
										assert abs(result_mean.item() - 0.08_78		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.full_loop(set_alpha_to_one=A_  ,   beta_start=0.01		)
										_UpperCAmelCase    :						Dict							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[int]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 2_30.03_99		) < 1e-2
										assert abs(result_mean.item() - 0.29_95		) < 1e-3





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.full_loop(set_alpha_to_one=A_  ,   beta_start=0.01		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Dict							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 1_86.94_82		) < 1e-2
										assert abs(result_mean.item() - 0.24_34		) < 1e-3


