


'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: str       =    CanineTokenizer
							A_				: Tuple       =    False






							def UpperCAmelCase_      (       self    ):
											super().setUp()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= CanineTokenizer()
											tokenizer.save_pretrained(self.tmpdirname    )






							@cached_property
							def UpperCAmelCase_      (       self    ):
											return CanineTokenizer.from_pretrained("""google/canine-s"""    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **_SCREAMING_SNAKE_CASE    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 1024
											return tokenizer






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.canine_tokenizer
											_SCREAMING_SNAKE_CASE					:			Tuple							= ["""Life is like a box of chocolates.""", """You never know what you\'re gonna get."""]
											# fmt: off
											_SCREAMING_SNAKE_CASE					:			Dict							= [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
											# fmt: on
											_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(_SCREAMING_SNAKE_CASE       ,   padding=_SCREAMING_SNAKE_CASE       ,   return_tensors="""pt"""    )
											self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )

											_SCREAMING_SNAKE_CASE					:			Any							= list(batch.input_ids.numpy()[0]    )

											self.assertListEqual(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )

											self.assertEqual((2, 39)       ,   batch.input_ids.shape    )
											self.assertEqual((2, 39)       ,   batch.attention_mask.shape    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.canine_tokenizer
											_SCREAMING_SNAKE_CASE					:			Tuple							= ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= tokenizer(_SCREAMING_SNAKE_CASE       ,   padding=_SCREAMING_SNAKE_CASE       ,   return_tensors="""pt"""    )
											# check if input_ids, attention_mask and token_type_ids are returned
											self.assertIn("""input_ids"""       ,   _SCREAMING_SNAKE_CASE    )
											self.assertIn("""attention_mask"""       ,   _SCREAMING_SNAKE_CASE    )
											self.assertIn("""token_type_ids"""       ,   _SCREAMING_SNAKE_CASE    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= self.canine_tokenizer
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    """What\'s the weater?""",
											    """It\'s about 25 degrees.""",
											]
											_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(
											    text_target=_SCREAMING_SNAKE_CASE       ,   max_length=32       ,   padding="""max_length"""       ,   truncation=_SCREAMING_SNAKE_CASE       ,   return_tensors="""pt"""    )
											self.assertEqual(32       ,   targets["""input_ids"""].shape[1]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			self.assertNotEqual(tokenizer.model_max_length       ,   42    )

        # Now let's start the test
											_SCREAMING_SNAKE_CASE					:			int							= self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			# Isolate this from the other tests because we save additional tokens/etc
																			_SCREAMING_SNAKE_CASE					:			int							= tempfile.mkdtemp()

																			_SCREAMING_SNAKE_CASE					:			Tuple							= """ He is very happy, UNwant\u00E9d,running"""
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )

																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			Any							= after_tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )

																			shutil.rmtree(_SCREAMING_SNAKE_CASE    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_tokenizers(model_max_length=42    )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			# Isolate this from the other tests because we save additional tokens/etc
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= tempfile.mkdtemp()

																			_SCREAMING_SNAKE_CASE					:			int							= """ He is very happy, UNwant\u00E9d,running"""

																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= tokenizer.additional_special_tokens

																			# We can add a new special token for Canine as follows:
																			_SCREAMING_SNAKE_CASE					:			int							= chr(0XE007    )
																			additional_special_tokens.append(_SCREAMING_SNAKE_CASE    )
																			tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}    )
																			_SCREAMING_SNAKE_CASE					:			List[Any]							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )

																			_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			List[str]							= after_tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )
																			self.assertIn(_SCREAMING_SNAKE_CASE       ,   after_tokenizer.additional_special_tokens    )
																			self.assertEqual(after_tokenizer.model_max_length       ,   42    )

																			_SCREAMING_SNAKE_CASE					:			Any							= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE       ,   model_max_length=43    )
																			self.assertEqual(tokenizer.model_max_length       ,   43    )

																			shutil.rmtree(_SCREAMING_SNAKE_CASE    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE    )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= self.get_clean_sequence(_SCREAMING_SNAKE_CASE    )

																			# a special token for Canine can be defined as follows:
																			_SCREAMING_SNAKE_CASE					:			List[str]							= 0XE005
																			_SCREAMING_SNAKE_CASE					:			List[str]							= chr(_SCREAMING_SNAKE_CASE    )

																			tokenizer.add_special_tokens({"""cls_token""": special_token}    )
																			_SCREAMING_SNAKE_CASE					:			Any							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			self.assertEqual(len(_SCREAMING_SNAKE_CASE    )       ,   1    )

																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tokenizer.decode(ids + encoded_special_token       ,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			Any							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )

																			_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			List[Any]							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			self.assertEqual(_SCREAMING_SNAKE_CASE       ,   input_encoded + special_token_id    )

																			_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer.decode(_SCREAMING_SNAKE_CASE       ,   skip_special_tokens=_SCREAMING_SNAKE_CASE    )
																			self.assertTrue(special_token not in decoded    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE    )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			_SCREAMING_SNAKE_CASE					:			List[str]							= chr(0XE005    )
																			_SCREAMING_SNAKE_CASE					:			List[str]							= chr(0XE006    )

																			# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																			tokenizer.add_tokens([SPECIAL_TOKEN_1]       ,   special_tokens=_SCREAMING_SNAKE_CASE    )
																			# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																			# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																			tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}    )

																			_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			int							= tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )

																			self.assertEqual(len(_SCREAMING_SNAKE_CASE    )       ,   1    )
																			self.assertEqual(len(_SCREAMING_SNAKE_CASE    )       ,   1    )
																			self.assertEqual(token_a[0]       ,   _SCREAMING_SNAKE_CASE    )
																			self.assertEqual(token_a[0]       ,   _SCREAMING_SNAKE_CASE    )






							@require_tokenizers
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE    )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			# a special token for Canine can be defined as follows:
																			_SCREAMING_SNAKE_CASE					:			str							= 0XE006
																			_SCREAMING_SNAKE_CASE					:			str							= chr(_SCREAMING_SNAKE_CASE    )

																			_SCREAMING_SNAKE_CASE					:			Dict							= AddedToken(_SCREAMING_SNAKE_CASE       ,   lstrip=_SCREAMING_SNAKE_CASE    )
																			tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}    )

																			with tempfile.TemporaryDirectory() as tmp_dir_name:
																							tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
																							tokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= []
											if self.test_slow_tokenizer:
															tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

											if self.test_rust_tokenizer:
															tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

											for tokenizer_class, tokenizer_utils in tokenizer_list:
															with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer_utils.save_pretrained(_SCREAMING_SNAKE_CASE    )

																			with open(os.path.join(_SCREAMING_SNAKE_CASE       ,   """special_tokens_map.json"""    )       ,   encoding="""utf-8"""    ) as json_file:
																							_SCREAMING_SNAKE_CASE					:			Dict							= json.load(_SCREAMING_SNAKE_CASE    )

																			with open(os.path.join(_SCREAMING_SNAKE_CASE       ,   """tokenizer_config.json"""    )       ,   encoding="""utf-8"""    ) as json_file:
																							_SCREAMING_SNAKE_CASE					:			Optional[int]							= json.load(_SCREAMING_SNAKE_CASE    )

																			# a special token for Canine can be defined as follows:
																			_SCREAMING_SNAKE_CASE					:			int							= 0XE006
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= chr(_SCREAMING_SNAKE_CASE    )

																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [new_token_a]
																			_SCREAMING_SNAKE_CASE					:			Any							= [new_token_a]

																			with open(os.path.join(_SCREAMING_SNAKE_CASE       ,   """special_tokens_map.json"""    )       ,   """w"""       ,   encoding="""utf-8"""    ) as outfile:
																							json.dump(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )
																			with open(os.path.join(_SCREAMING_SNAKE_CASE       ,   """tokenizer_config.json"""    )       ,   """w"""       ,   encoding="""utf-8"""    ) as outfile:
																							json.dump(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )

																			# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																			# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																			# "special_tokens_map.json" files
																			_SCREAMING_SNAKE_CASE					:			int							= tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,   extra_ids=0    )
																			self.assertIn(_SCREAMING_SNAKE_CASE       ,   tokenizer_without_change_in_init.additional_special_tokens    )
																			# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																			self.assertEqual(
																			    [new_token_a]       ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
																			        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]    )    )       ,   )

																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0XE007
																			_SCREAMING_SNAKE_CASE					:			int							= chr(_SCREAMING_SNAKE_CASE    )
																			# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [AddedToken(_SCREAMING_SNAKE_CASE       ,   lstrip=_SCREAMING_SNAKE_CASE    )]
																			_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer_class.from_pretrained(
																			    _SCREAMING_SNAKE_CASE       ,   additional_special_tokens=_SCREAMING_SNAKE_CASE       ,   extra_ids=0    )

																			self.assertIn(_SCREAMING_SNAKE_CASE       ,   tokenizer.additional_special_tokens    )
																			# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																			self.assertEqual(
																			    [new_token_a]       ,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]    )    )    )






							@require_tokenizers
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE    )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= """hello world"""
																			if self.space_between_special_tokens:
																							_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """[CLS] hello world [SEP]"""
																			else:
																							_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= input
																			_SCREAMING_SNAKE_CASE					:			int							= tokenizer.encode(_SCREAMING_SNAKE_CASE       ,   add_special_tokens=_SCREAMING_SNAKE_CASE    )
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= tokenizer.decode(_SCREAMING_SNAKE_CASE       ,   spaces_between_special_tokens=self.space_between_special_tokens    )
																			self.assertIn(_SCREAMING_SNAKE_CASE       ,   [output, output.lower()]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																			_SCREAMING_SNAKE_CASE					:			int							= [
																			    """bos_token""",
																			    """eos_token""",
																			    """unk_token""",
																			    """sep_token""",
																			    """pad_token""",
																			    """cls_token""",
																			    """mask_token""",
																			]

											_SCREAMING_SNAKE_CASE					:			int							= """a"""
											_SCREAMING_SNAKE_CASE					:			Any							= ord(_SCREAMING_SNAKE_CASE    )

											for attr in attributes_list:
															setattr(_SCREAMING_SNAKE_CASE       ,   attr + """_id"""       ,   _SCREAMING_SNAKE_CASE    )
															self.assertEqual(getattr(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )       ,   _SCREAMING_SNAKE_CASE    )
															self.assertEqual(getattr(_SCREAMING_SNAKE_CASE       ,   attr + """_id"""    )       ,   _SCREAMING_SNAKE_CASE    )

															setattr(_SCREAMING_SNAKE_CASE       ,   attr + """_id"""       ,   _SCREAMING_SNAKE_CASE    )
															self.assertEqual(getattr(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE    )       ,   _SCREAMING_SNAKE_CASE    )
															self.assertEqual(getattr(_SCREAMING_SNAKE_CASE       ,   attr + """_id"""    )       ,   _SCREAMING_SNAKE_CASE    )

											setattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens_ids"""       ,   []    )
											self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens"""    )       ,   []    )
											self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens_ids"""    )       ,   []    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0XE006
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= chr(_SCREAMING_SNAKE_CASE    )
											setattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens_ids"""       ,   [additional_special_token_id]    )
											self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens"""    )       ,   [additional_special_token]    )
											self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE       ,   """additional_special_tokens_ids"""    )       ,   [additional_special_token_id]    )






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											pass








from typing import Any

def 			lowerCAmelCase__					(     a__:						list     ,    a__:						list     ,    a__:						dict     ,    a__:						dict     ,    a__:						dict     ,    )    ->						list:


							'''simple docstring'''





							_validation(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							# Creates data structures and fill initial step
							_UpperCAmelCase											=     {}
							_UpperCAmelCase											=     {}
							for state in states_space:
														_UpperCAmelCase											=     observations_space[0]
														_UpperCAmelCase											=     (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														_UpperCAmelCase											=     None

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

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

																					_UpperCAmelCase											=     arg_max

    # The final observation
							_UpperCAmelCase											=     observations_space[len(a__			) - 1]

							# argmax for given final observation
							_UpperCAmelCase											=     ''
							_UpperCAmelCase											=     -1
							for k_state in states_space:
														_UpperCAmelCase											=     probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					_UpperCAmelCase											=     probability
																					_UpperCAmelCase											=     k_state
							_UpperCAmelCase											=     arg_max

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

							return result

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_not_empty(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							_validate_lists(a__     ,    a__			)
							_validate_dicts(
							    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





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

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any			)    ->						None:


							'''simple docstring'''





							_validate_list(a__     ,    'observations_space'			)
							_validate_list(a__     ,    'states_space'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a list'''
														raise ValueError(a__			)
							else:
														for x in _object:
																					if not isinstance(a__     ,    a__			):
																												_UpperCAmelCase											=     F'''{var_name} must be a list of strings'''
																												raise ValueError(a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_dict(a__     ,    'initial_probabilities'     ,    a__			)
							_validate_nested_dict(a__     ,    'transition_probabilities'			)
							_validate_nested_dict(a__     ,    'emission_probabilities'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							_validate_dict(_object     ,    a__     ,    a__			)
							for x in _object.values():
														_validate_dict(a__     ,    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str     ,    a__:						type     ,    a__:						bool = False			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a dict'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object			):
														_UpperCAmelCase											=     F'''{var_name} all keys must be strings'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object.values()			):
														_UpperCAmelCase											=     'nested dictionary ' if nested else ''
														_UpperCAmelCase											=     F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a__			)


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

		testmod()
