import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 		__A       (__magic_name__   ):
       snake_case       :Dict				    =					(PNDMScheduler,)
       snake_case       :List[Any]				    =					(("num_inference_steps", 50),)






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		{
                  "num_train_timesteps": 10_00,
                  "beta_start": 0.0_0_0_1,
                  "beta_end": 0.0_2,
                  "beta_schedule": "linear",
              }

              config.update(**UpperCamelCase_      )
              return config






       def 			_snake_case						(      self				,     UpperCamelCase_=0				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		dict(self.forward_default_kwargs      )
              __UpperCAmelCase  :				Dict       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		self.dummy_sample
              __UpperCAmelCase  :				Optional[Any]       				=		0.1 * sample
              __UpperCAmelCase  :				Any       				=		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				List[Any]       				=		self.get_scheduler_config(**UpperCamelCase_      )
                     __UpperCAmelCase  :				Tuple       				=		scheduler_class(**UpperCamelCase_      )
                     scheduler.set_timesteps(UpperCamelCase_      )
                     # copy over dummy past residuals
                     __UpperCAmelCase  :				List[Any]       				=		dummy_past_residuals[:]

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(UpperCamelCase_      )
                            __UpperCAmelCase  :				int       				=		scheduler_class.from_pretrained(UpperCamelCase_      )
                            new_scheduler.set_timesteps(UpperCamelCase_      )
                            # copy over dummy past residuals
                            __UpperCAmelCase  :				Any       				=		dummy_past_residuals[:]

                     __UpperCAmelCase  :				Any       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				Dict       				=		new_scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				List[str]       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				int       				=		new_scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self				,     UpperCamelCase_=0				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		dict(self.forward_default_kwargs      )
              __UpperCAmelCase  :				Optional[Any]       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		self.dummy_sample
              __UpperCAmelCase  :				int       				=		0.1 * sample
              __UpperCAmelCase  :				Dict       				=		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Optional[int]       				=		scheduler_class(**UpperCamelCase_      )
                     scheduler.set_timesteps(UpperCamelCase_      )

                     # copy over dummy past residuals (must be after setting timesteps)
                     __UpperCAmelCase  :				Union[str, Any]       				=		dummy_past_residuals[:]

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(UpperCamelCase_      )
                            __UpperCAmelCase  :				List[Any]       				=		scheduler_class.from_pretrained(UpperCamelCase_      )
                            # copy over dummy past residuals
                            new_scheduler.set_timesteps(UpperCamelCase_      )

                            # copy over dummy past residual (must be after setting timesteps)
                            __UpperCAmelCase  :				Optional[int]       				=		dummy_past_residuals[:]

                     __UpperCAmelCase  :				str       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				str       				=		new_scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				Any       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				List[str]       				=		new_scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config(**UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		scheduler_class(**UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[Any]       				=		10
              __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_model()
              __UpperCAmelCase  :				Dict       				=		self.dummy_sample_deter
              scheduler.set_timesteps(UpperCamelCase_      )

              for i, t in enumerate(scheduler.prk_timesteps      ):
                     __UpperCAmelCase  :				str       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				Union[str, Any]       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

              for i, t in enumerate(scheduler.plms_timesteps      ):
                     __UpperCAmelCase  :				Any       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				List[Any]       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

              return sample






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		dict(self.forward_default_kwargs      )

              __UpperCAmelCase  :				Optional[int]       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				str       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Tuple       				=		scheduler_class(**UpperCamelCase_      )

                     __UpperCAmelCase  :				Any       				=		self.dummy_sample
                     __UpperCAmelCase  :				List[str]       				=		0.1 * sample

                     if num_inference_steps is not None and hasattr(UpperCamelCase_				,     "set_timesteps"      ):
                            scheduler.set_timesteps(UpperCamelCase_      )
                     elif num_inference_steps is not None and not hasattr(UpperCamelCase_				,     "set_timesteps"      ):
                            __UpperCAmelCase  :				List[str]       				=		num_inference_steps

                     # copy over dummy past residuals (must be done after set_timesteps)
                     __UpperCAmelCase  :				int       				=		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                     __UpperCAmelCase  :				Optional[Any]       				=		dummy_past_residuals[:]

                     __UpperCAmelCase  :				Dict       				=		scheduler.step_prk(UpperCamelCase_				,     0				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				int       				=		scheduler.step_prk(UpperCamelCase_				,     1				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				Optional[int]       				=		scheduler.step_plms(UpperCamelCase_				,     0				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				Union[str, Any]       				=		scheduler.step_plms(UpperCamelCase_				,     1				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self      ):
              for timesteps in [1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase_      )

              __UpperCAmelCase  :				str       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				str       				=		self.get_scheduler_config(steps_offset=1      )
              __UpperCAmelCase  :				List[str]       				=		scheduler_class(**UpperCamelCase_      )
              scheduler.set_timesteps(10      )
              assert torch.equal(
                  scheduler.timesteps				,     torch.LongTensor(
                      [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]      )				,     )






       def 			_snake_case						(      self      ):
              for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]				,     [0.0_0_2, 0.0_2]      ):
                     self.check_over_configs(beta_start=UpperCamelCase_				,     beta_end=UpperCamelCase_      )






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






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






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






       def 			_snake_case						(      self      ):
              for t, num_inference_steps in zip([1, 5, 10]				,     [10, 50, 1_00]      ):
                     self.check_over_forward(num_inference_steps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
              __UpperCAmelCase  :				Dict       				=		27

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_sample
                     __UpperCAmelCase  :				int       				=		0.1 * sample

                     __UpperCAmelCase  :				Dict       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				str       				=		scheduler_class(**UpperCamelCase_      )

                     scheduler.set_timesteps(UpperCamelCase_      )

                     # 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  :				int       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample






       def 			_snake_case						(      self      ):
              with self.assertRaises(UpperCamelCase_      ):
                     __UpperCAmelCase  :				Tuple       				=		self.scheduler_classes[0]
                     __UpperCAmelCase  :				List[str]       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Optional[Any]       				=		scheduler_class(**UpperCamelCase_      )

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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.full_loop()
              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				int       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_98.13_18      ) < 1E-2
              assert abs(result_mean.item() - 0.2_5_8_0      ) < 1E-3






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.full_loop(prediction_type="v_prediction"      )
              __UpperCAmelCase  :				Any       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 67.39_86      ) < 1E-2
              assert abs(result_mean.item() - 0.0_8_7_8      ) < 1E-3






       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				str       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[str]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 2_30.03_99      ) < 1E-2
              assert abs(result_mean.item() - 0.2_9_9_5      ) < 1E-3







       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				Dict       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				Optional[int]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Tuple       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_86.94_82      ) < 1E-2
              assert abs(result_mean.item() - 0.2_4_3_4      ) < 1E-3







'''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


_a   :	Union[str, Any] 							=   logging.get_logger(__name__)

_a   :	Any 							=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_a   :	Tuple 							=   {
    "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"
        ),
    },
}

_a   :	Dict 							=   {
    "Salesforce/codegen-350M-mono": 2048,
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[Any]				    =					VOCAB_FILES_NAMES
       snake_case       :str				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Optional[Any]				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Tuple				    =					["input_ids", "attention_mask"]
       snake_case       :Dict				    =					CodeGenTokenizer






       def __init__(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              super().__init__(
                  UpperCamelCase_				,     UpperCamelCase_				,     tokenizer_file=UpperCamelCase_				,     unk_token=UpperCamelCase_				,     bos_token=UpperCamelCase_				,     eos_token=UpperCamelCase_				,     add_prefix_space=UpperCamelCase_				,     **UpperCamelCase_				,     )

              if kwargs.pop("add_bos_token"				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				int       				=		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  :				Any       				=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
              if pre_tok_state.get("add_prefix_space"				,     UpperCamelCase_      ) != add_prefix_space:
                     __UpperCAmelCase  :				str       				=		getattr(UpperCamelCase_				,     pre_tok_state.pop("type"      )      )
                     __UpperCAmelCase  :				Optional[int]       				=		add_prefix_space
                     __UpperCAmelCase  :				Tuple       				=		pre_tok_class(**UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		add_prefix_space






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[Any]       				=		kwargs.get("is_split_into_words"				,     UpperCamelCase_      )
              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(*UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		kwargs.get("is_split_into_words"				,     UpperCamelCase_      )

              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(*UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		self._tokenizer.model.save(UpperCamelCase_				,     name=UpperCamelCase_      )
              return tuple(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				str       				=		super().decode(
                  token_ids=UpperCamelCase_				,     skip_special_tokens=UpperCamelCase_				,     clean_up_tokenization_spaces=UpperCamelCase_				,     **UpperCamelCase_				,     )

              if truncate_before_pattern is not None and len(UpperCamelCase_      ) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.truncate(UpperCamelCase_				,     UpperCamelCase_      )

              return decoded_text







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              def find_re(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Dict       				=		pattern.search(UpperCamelCase_				,     UpperCamelCase_      )
                     return m.start() if m else -1

              __UpperCAmelCase  :				List[str]       				=		[re.compile(UpperCamelCase_				,     re.MULTILINE      ) for pattern in truncate_before_pattern]

              __UpperCAmelCase  :				Optional[Any]       				=		list(re.finditer("^print"				,     UpperCamelCase_				,     re.MULTILINE      )      )

              if len(UpperCamelCase_      ) > 1:
                     __UpperCAmelCase  :				List[Any]       				=		completion[: prints[1].start()]

              __UpperCAmelCase  :				Tuple       				=		list(re.finditer("^def"				,     UpperCamelCase_				,     re.MULTILINE      )      )

              if len(UpperCamelCase_      ) > 1:
                     __UpperCAmelCase  :				Union[str, Any]       				=		completion[: defs[1].start()]

              __UpperCAmelCase  :				Dict       				=		0

              __UpperCAmelCase  :				Dict       				=		[
                  pos for pos in [find_re(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ) for terminal in terminals] if pos != -1
              ]

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






