




'''simple docstring'''







import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   A     ='0.12'  # assumed parallelism: 8


if is_torch_available():
   import torch





def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]    ,   _a	:							Dict=None     ):
    if rng is None:
        UpperCAmelCase			       =		random.Random()

    UpperCAmelCase			       =		1
    for dim in shape:
        total_dims *= dim

    UpperCAmelCase			       =		[]
    for _ in range(_a     ):
        values.append(rng.randint(0    ,   vocab_size - 1     )     )

    UpperCAmelCase			       =		np.array(_a    ,   dtype=jnp.intaa     ).reshape(_a     )

    return output





def 						snake_case_							(_a	:							Any    ,   _a	:							int=None     ):
    UpperCAmelCase			       =		ids_tensor(_a    ,   vocab_size=2    ,   rng=_a     )
    # make sure that at least one token is attended to for each batch
    UpperCAmelCase			       =		1
    return attn_mask


@require_flax
class 			_a  :
       __a				:     Dict   				=					None
       __a				:     List[Any]   				=					()
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           # cut to half length & take max batch_size 3
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		inputs['''input_ids'''].shape[-1] // 2
           UpperCAmelCase			       =		inputs['''input_ids'''][:max_batch_size, :sequence_length]

           UpperCAmelCase			       =		jnp.ones_like(lowercase				)
           UpperCAmelCase			       =		attention_mask[:max_batch_size, :sequence_length]

           # generate max 5 tokens
           UpperCAmelCase			       =		input_ids.shape[-1] + 5
           if config.eos_token_id is not None and config.pad_token_id is None:
               # hack to allow generate for models such as GPT2 as is done in `generate()`
               UpperCAmelCase			       =		config.eos_token_id
           return config, input_ids, attention_mask, max_length
       @is_pt_flax_cross_test
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		0

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCAmelCase			       =		getattr(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		pt_model_class(lowercase				).eval()
               UpperCAmelCase			       =		load_flax_weights_in_pytorch_model(lowercase   ,				flax_model.params				)

               UpperCAmelCase			       =		flax_model.generate(lowercase				).sequences
               UpperCAmelCase			       =		pt_model.generate(torch.tensor(lowercase   ,				dtype=torch.long				)				)

               if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   UpperCAmelCase			       =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

               self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,				flax_generation_outputs.tolist()				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		2

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[0]   ,				input_ids.shape[0] * config.num_return_sequences				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		0.8
           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		0.3
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)







       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)






@require_flax
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
           UpperCAmelCase			       =		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

           UpperCAmelCase			       =		'''Hello world'''
           UpperCAmelCase			       =		tokenizer(lowercase   ,				return_tensors='''np'''				).input_ids

           # typos are quickly detected (the correct argument is `do_sample`)
           with self.assertRaisesRegex(lowercase   ,				'''do_samples'''				):
               model.generate(lowercase   ,				do_samples=lowercase				)

           # arbitrary arguments that will not be used anywhere are also not accepted
           with self.assertRaisesRegex(lowercase   ,				'''foo'''				):
               UpperCAmelCase			       =		{'''foo''': '''bar'''}
               model.generate(lowercase   ,				**lowercase				)










'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





def 						snake_case_							(_a	:							dict    ,   _a	:							str    ,   _a	:							set    ,   _a	:							set    ,   _a	:							dict    ,   _a	:							dict    ,   _a	:							PriorityQueue    ,   _a	:							dict    ,   _a	:							float | int    ,   ):
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        UpperCAmelCase			       =		cst_fwd.get(_a    ,   np.inf     )
        UpperCAmelCase			       =		cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt)     )
            UpperCAmelCase			       =		new_cost_f
            UpperCAmelCase			       =		v
        if nxt in visited_backward:
            if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                UpperCAmelCase			       =		cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		{source: 0}
    UpperCAmelCase			       =		{destination: 0}
    UpperCAmelCase			       =		{source: None}
    UpperCAmelCase			       =		{destination: None}
    UpperCAmelCase			       =		PriorityQueue()
    UpperCAmelCase			       =		PriorityQueue()

    UpperCAmelCase			       =		np.inf

    queue_forward.put((0, source)     )
    queue_backward.put((0, destination)     )

    if source == destination:
        return 0

    while not queue_forward.empty() and not queue_backward.empty():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
            break

    if shortest_distance != np.inf:
        UpperCAmelCase			       =		shortest_distance
    return shortest_path_distance


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()




