from typing import Any





def  _lowerCamelCase					(   a_				: list						,			a_				: list						,			a_				: dict						,			a_				: dict						,			a_				: dict						,			):
			_validation(
			    a_						,			a_						,			a_						,			a_						,			a_						,			)
			# Creates data structures and fill initial step
			lowerCamelCase						:dict						  =     {}
			lowerCamelCase						:dict						  =     {}
			for state in states_space:
						lowerCamelCase						:Dict						  =     observations_space[0]
						lowerCamelCase						:Optional[int]						  =     (
						    initial_probabilities[state] * emission_probabilities[state][observation]
						)
						lowerCamelCase						:str						  =     None

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

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

									lowerCamelCase						:int						  =     arg_max

    # The final observation
			lowerCamelCase						:str						  =     observations_space[len(a_) - 1]

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

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

			return result





def  _lowerCamelCase					(   a_				: Any						,			a_				: Any						,			a_				: Any						,			a_				: Any						,			a_				: Any						,			):
			_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						,			):
			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):
			_validate_list(a_						,			'''observations_space''')
			_validate_list(a_						,			'''states_space''')





def  _lowerCamelCase					(   a_				: Any						,			a_				: str):
			if not isinstance(_object						,			a_):
						lowerCamelCase						:Dict						  =     F"{var_name} must be a list"
						raise ValueError(a_)
			else:
						for x in _object:
									if not isinstance(a_						,			a_):
												lowerCamelCase						:List[str]						  =     F"{var_name} must be a list of strings"
												raise ValueError(a_)





def  _lowerCamelCase					(   a_				: Any						,			a_				: Any						,			a_				: Any						,			):
			_validate_dict(a_						,			'''initial_probabilities'''						,			a_)
			_validate_nested_dict(a_						,			'''transition_probabilities''')
			_validate_nested_dict(a_						,			'''emission_probabilities''')





def  _lowerCamelCase					(   a_				: Any						,			a_				: str):
			_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):
			if not isinstance(_object						,			a_):
						lowerCamelCase						:str						  =     F"{var_name} must be a dict"
						raise ValueError(a_)
			if not all(isinstance(a_						,			a_) for x in _object):
						lowerCamelCase						:Tuple						  =     F"{var_name} all keys must be strings"
						raise ValueError(a_)
			if not all(isinstance(a_						,			a_) for x in _object.values()):
						lowerCamelCase						:Dict						  =     '''nested dictionary ''' if nested else ''''''
						lowerCamelCase						:Optional[int]						  =     F"{var_name} {nested_text}all values must be {value_type.__name__}"
						raise ValueError(a_)


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

			testmod()




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   _lowerCAmelCase		(     unittest.TestCase				):







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

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# 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
										lowerCamelCase						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

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

										# 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
										lowerCamelCase						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







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

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

										# The streamer will timeout after 0.001 seconds, so an exception will be raised
										with self.assertRaises(__snake_case		):
													lowerCamelCase						:Dict						  =     ''''''
													for new_text in streamer:
																streamer_text += new_text



