






"""simple docstring"""



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM





@require_torch
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowercase_ : List[str]							       =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(A   )
							lowercase_ : Any							       =   -1

							lowercase_ : List[Any]							       =   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size   ).to(A   )
							lowercase_ : str							       =   model.generate(A    ,						max_new_tokens=10    ,						do_sample=A   )
							lowercase_ : Optional[int]							       =   tokenizer.decode(greedy_ids[0]   )

							with CaptureStdout() as cs:
									lowercase_ : Dict							       =   TextStreamer(A   )
									model.generate(A    ,						max_new_tokens=10    ,						do_sample=A    ,						streamer=A   )
							# The greedy text should be printed to stdout, except for the final "\n" in the streamer
							lowercase_ : Any							       =   cs.out[:-1]

							self.assertEqual(A    ,						A   )




					def   A  (				self     :						List[Any]   )  ->  int:
							lowercase_ : int							       =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowercase_ : List[Any]							       =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(A   )
							lowercase_ : Optional[int]							       =   -1

							lowercase_ : Dict							       =   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size   ).to(A   )
							lowercase_ : List[str]							       =   model.generate(A    ,						max_new_tokens=10    ,						do_sample=A   )
							lowercase_ : List[str]							       =   tokenizer.decode(greedy_ids[0]   )

							lowercase_ : Any							       =   TextIteratorStreamer(A   )
							lowercase_ : int							       =   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
							lowercase_ : int							       =   Thread(target=model.generate    ,						kwargs=A   )
							thread.start()
							lowercase_ : List[Any]							       =   ''''''
							for new_text in streamer:
									streamer_text += new_text

							self.assertEqual(A    ,						A   )




					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Dict							       =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowercase_ : List[Any]							       =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(A   )
							lowercase_ : Any							       =   -1

							lowercase_ : List[str]							       =   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size   ).to(A   )
							lowercase_ : List[str]							       =   model.generate(A    ,						max_new_tokens=10    ,						do_sample=A   )
							lowercase_ : Union[str, Any]							       =   greedy_ids[:, input_ids.shape[1] :]
							lowercase_ : Optional[int]							       =   tokenizer.decode(new_greedy_ids[0]   )

							with CaptureStdout() as cs:
									lowercase_ : Union[str, Any]							       =   TextStreamer(A    ,						skip_prompt=A   )
									model.generate(A    ,						max_new_tokens=10    ,						do_sample=A    ,						streamer=A   )
							# The greedy text should be printed to stdout, except for the final "\n" in the streamer
							lowercase_ : List[str]							       =   cs.out[:-1]

							self.assertEqual(A    ,						A   )




					def   A  (				self     :						Any   )  ->  Any:
							# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
							# with actual models -- the dummy models' tokenizers are not aligned with their models, and
							# `skip_special_tokens=True` has no effect on them
							lowercase_ : int							       =   AutoTokenizer.from_pretrained('''distilgpt2'''   )
							lowercase_ : str							       =   AutoModelForCausalLM.from_pretrained('''distilgpt2'''   ).to(A   )
							lowercase_ : str							       =   -1

							lowercase_ : Dict							       =   torch.ones((1, 5)    ,						device=A   ).long() * model.config.bos_token_id
							with CaptureStdout() as cs:
									lowercase_ : List[str]							       =   TextStreamer(A    ,						skip_special_tokens=A   )
									model.generate(A    ,						max_new_tokens=1    ,						do_sample=A    ,						streamer=A   )

							# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
							# re-tokenized, must only contain one token
							lowercase_ : str							       =   cs.out[:-1]  # Remove the final "\n"
							lowercase_ : Union[str, Any]							       =   tokenizer(A    ,						return_tensors='''pt'''   )
							self.assertEqual(streamer_text_tokenized.input_ids.shape    ,						(1, 1)   )






					def   A  (				self     :						List[str]   )  ->  List[Any]:
							lowercase_ : List[Any]							       =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowercase_ : Optional[Any]							       =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(A   )
							lowercase_ : Optional[int]							       =   -1

							lowercase_ : Optional[Any]							       =   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size   ).to(A   )
							lowercase_ : Optional[Any]							       =   TextIteratorStreamer(A    ,						timeout=0.001   )
							lowercase_ : List[Any]							       =   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
							lowercase_ : Optional[int]							       =   Thread(target=model.generate    ,						kwargs=A   )
							thread.start()

							# The streamer will timeout after 0.001 seconds, so an exception will be raised
							with self.assertRaises(A   ):
									lowercase_ : Union[str, Any]							       =   ''''''
									for new_text in streamer:
											streamer_text += new_text




'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







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

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

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

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

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

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

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      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 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


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

  testmod()

