




'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 		SCREAMING_SNAKE_CASE__  :





			def __init__(      self						:    List[Any]      ,		a_						:    Optional[Any]=2      ,		a_						:    Tuple=3      ,		a_						:    Any=64      ,		a_						:    str=None							):



						"""simple docstring"""




						__snake_case			=		np.random.default_rng(a_							)
						__snake_case			=		length
						__snake_case			=		rng.normal(size=(length,)							).astype(np.floataa							)
						__snake_case			=		a * self.x + b + rng.normal(scale=0.1      ,		size=(length,)							).astype(np.floataa							)





			def __len__(      self						:    Optional[Any]							):



						"""simple docstring"""




						return self.length






			def __getitem__(      self						:    Optional[Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




						return {"x": self.x[i], "y": self.y[i]}







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Any      ,		a_						:    List[str]=0      ,		a_						:    Union[str, Any]=0      ,		a_						:    Tuple=False							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
						__snake_case			=		torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
						__snake_case			=		True






			def 							A (      self						:    List[str]      ,		a_						:    int=None							):



						"""simple docstring"""




						if self.first_batch:
									print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
									__snake_case			=		False
						return x * self.a[0] + self.b[0]







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Dict      ,		a_						:    Dict=0      ,		a_						:    Any=0      ,		a_						:    Optional[Any]=False							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		True






			def 							A (      self						:    Dict      ,		a_						:    Any=None							):



						"""simple docstring"""




						if self.first_batch:
									print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
									__snake_case			=		False
						return x * self.a + self.b







def __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]		,				_UpperCAmelCase		:   int = 16						)      ->      str:
			from datasets import load_dataset
			from transformers import AutoTokenizer

			__snake_case			=		AutoTokenizer.from_pretrained("bert-base-cased"						)
			__snake_case			=		{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			__snake_case			=		load_dataset("csv"		,				data_files=_UpperCAmelCase						)
			__snake_case			=		datasets["train"].unique("label"						)

			__snake_case			=		{v: i for i, v in enumerate(_UpperCAmelCase						)}

			def tokenize_function(_UpperCAmelCase		:   List[str]						):
						# max_length=None => use the model max length (it's actually the default)
						__snake_case			=		tokenizer(
						    examples["sentence1"]		,				examples["sentence2"]		,				truncation=_UpperCAmelCase		,				max_length=_UpperCAmelCase		,				padding="max_length"						)
						if "label" in examples:
									__snake_case			=		[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__snake_case			=		datasets.map(
			    _UpperCAmelCase		,				batched=_UpperCAmelCase		,				remove_columns=["sentence1", "sentence2", "label"]		,				)

			def collate_fn(_UpperCAmelCase		:   Optional[Any]						):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCAmelCase		,				padding="max_length"		,				max_length=1_28		,				return_tensors="pt"						)
						return tokenizer.pad(_UpperCAmelCase		,				padding="longest"		,				return_tensors="pt"						)

			# Instantiate dataloaders.
			__snake_case			=		DataLoader(tokenized_datasets["train"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=2						)
			__snake_case			=		DataLoader(tokenized_datasets["validation"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=1						)

			return train_dataloader, eval_dataloader







from typing import Any


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validation(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      # Creates data structures and fill initial step
      UpperCamelCase__     : dict											=					{}
      UpperCamelCase__     : dict											=					{}
      for state in states_space:
            UpperCamelCase__     : Optional[int]											=					observations_space[0]
            UpperCamelCase__     : Any											=					(
                initial_probabilities[state] * emission_probabilities[state][observation]
            )
            UpperCamelCase__     : Union[str, Any]											=					None

      # Fills the data structure with the probabilities of
      # different transitions and pointers to previous states
      for o in range(1			,		len(UpperCamelCase__		)		):
            UpperCamelCase__     : str											=					observations_space[o]
            UpperCamelCase__     : Union[str, Any]											=					observations_space[o - 1]
            for state in states_space:
                  # Calculates the argmax for probability function
                  UpperCamelCase__     : int											=					''''''
                  UpperCamelCase__     : List[str]											=					-1
                  for k_state in states_space:
                        UpperCamelCase__     : Union[str, Any]											=					(
                            probabilities[(k_state, prior_observation)]
                            * transition_probabilities[k_state][state]
                            * emission_probabilities[state][observation]
                        )
                        if probability > max_probability:
                              UpperCamelCase__     : Tuple											=					probability
                              UpperCamelCase__     : Union[str, Any]											=					k_state

            # Update probabilities and pointers dicts
                  UpperCamelCase__     : Tuple											=					(
                      probabilities[(arg_max, prior_observation)]
                      * transition_probabilities[arg_max][state]
                      * emission_probabilities[state][observation]
                  )

                  UpperCamelCase__     : Optional[Any]											=					arg_max

    # The final observation
      UpperCamelCase__     : List[str]											=					observations_space[len(UpperCamelCase__		) - 1]

      # argmax for given final observation
      UpperCamelCase__     : Dict											=					''''''
      UpperCamelCase__     : Tuple											=					-1
      for k_state in states_space:
            UpperCamelCase__     : Any											=					probabilities[(k_state, final_observation)]
            if probability > max_probability:
                  UpperCamelCase__     : List[str]											=					probability
                  UpperCamelCase__     : Tuple											=					k_state
      UpperCamelCase__     : Any											=					arg_max

      # Process pointers backwards
      UpperCamelCase__     : List[Any]											=					last_state
      UpperCamelCase__     : int											=					[]
      for o in range(len(UpperCamelCase__		) - 1			,		-1			,		-1		):
            result.append(UpperCamelCase__		)
            UpperCamelCase__     : int											=					pointers[previous, observations_space[o]]
      result.reverse()

      return result


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_not_empty(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      _validate_lists(UpperCamelCase__			,		UpperCamelCase__		)
      _validate_dicts(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      if not all(
          [
              observations_space,
              states_space,
              initial_probabilities,
              transition_probabilities,
              emission_probabilities,
          ]		):
            raise ValueError('''There\'s an empty parameter'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_list(UpperCamelCase__			,		'''observations_space'''		)
      _validate_list(UpperCamelCase__			,		'''states_space'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list'''
            raise ValueError(UpperCamelCase__		)
      else:
            for x in _object:
                  if not isinstance(UpperCamelCase__			,		UpperCamelCase__		):
                        UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list of strings'''
                        raise ValueError(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_dict(UpperCamelCase__			,		'''initial_probabilities'''			,		UpperCamelCase__		)
      _validate_nested_dict(UpperCamelCase__			,		'''transition_probabilities'''		)
      _validate_nested_dict(UpperCamelCase__			,		'''emission_probabilities'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_dict(_object			,		UpperCamelCase__			,		UpperCamelCase__		)
      for x in _object.values():
            _validate_dict(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[str]											=					f'''{var_name} must be a dict'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object		):
            UpperCamelCase__     : Dict											=					f'''{var_name} all keys must be strings'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object.values()		):
            UpperCamelCase__     : Optional[Any]											=					'''nested dictionary ''' if nested else ''''''
            UpperCamelCase__     : Optional[Any]											=					f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
            raise ValueError(UpperCamelCase__		)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


