





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

    snake_case_				=			'0.12'  # assumed parallelism: 8


if is_torch_available():
    import torch






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  List[str]       ,      snake_case_	:  str=None       )      ->				List[str]:
   if rng is None:
      __snake_case							       =   random.Random()

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

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

   __snake_case							       =   np.array(snake_case_       ,      dtype=jnp.intaa       ).reshape(snake_case_       )

   return output






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  List[str]=None       )      ->				Tuple:
   __snake_case							       =   ids_tensor(snake_case_       ,      vocab_size=2       ,      rng=snake_case_       )
   # make sure that at least one token is attended to for each batch
   __snake_case							       =   1
   return attn_mask

@require_flax
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				Optional[int]							=    None
      A_						:				Union[str, Any]							=    ()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

         __snake_case							       =   jnp.ones_like(a__      )
         __snake_case							       =   attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __snake_case							       =   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()`
            __snake_case							       =   config.eos_token_id
         return config, input_ids, attention_mask, max_length


      @is_pt_flax_cross_test
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   flax_model.generate(a__      ).sequences
            __snake_case							       =   pt_model.generate(torch.tensor(a__				,			dtype=torch.long      )      )

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               __snake_case							       =   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"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      int      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[0]				,			input_ids.shape[0] * config.num_return_sequences      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      str      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      Dict      ):





         """simple docstring"""



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

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

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


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

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

         __snake_case							       =   False
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

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


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

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

         __snake_case							       =   True
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

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


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

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

         __snake_case							       =   2
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

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

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

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






@require_flax
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Any      ):





         """simple docstring"""



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

         __snake_case							       =   '''Hello world'''
         __snake_case							       =   tokenizer(a__				,			return_tensors='''np'''      ).input_ids

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

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



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__   :			List[Any]												=    TypeVar("T")




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (position - 1) // 2




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 1




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 2

class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							List[str]  )     ->							None:
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __len__(		self		:							Optional[int]  )     ->							int:
						return self.elements






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.heap  )






		def  __A       (		self		:							Union[str, Any]  )     ->							bool:
						# Check if the priority queue is empty
						return self.elements == 0






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an element with given priority to the queue
						self.heap.append((elem, weight)  )
						__lowerCamelCase    					=	self.elements
						self.elements += 1
						self._bubble_up(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							T:
						# Remove and return the element with lowest weight (highest priority)
						if self.elements > 1:
										self._swap_nodes(0							,			self.elements - 1  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[0]
										self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return elem






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Update the weight of the given key
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase    					=	(elem, weight)
						if position > 0:
										__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
										if parent_weight > weight:
														self._bubble_up(SCREAMING_SNAKE_CASE__  )
										else:
														self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										self._bubble_down(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (upward movement) [to be used internally
						# only]
						__lowerCamelCase    					=	self.position_map[elem]
						if curr_pos == 0:
										return None
						__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
						if parent_weight > weight:
										self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										return self._bubble_up(SCREAMING_SNAKE_CASE__  )
						return None






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (downward movement) [to be used
						# internally only]
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase    					=	get_child_left_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	get_child_right_position(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements and child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < child_left_weight and child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										if child_left_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										return None
						if child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return None







		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Swap the nodes at the given positions
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase					,    __lowerCamelCase    					=	(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						__lowerCamelCase    					=	nodea_pos
						__lowerCamelCase    					=	nodea_pos





class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							Tuple  )     ->							None:
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.connections  )






		def __len__(		self		:							List[str]  )     ->							int:
						return self.nodes






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Add a node in the graph if it is not in the graph
						if node not in self.connections:
										__lowerCamelCase    					=	{}
										self.nodes += 1







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an edge between 2 nodes in the graph
						self.add_node(SCREAMING_SNAKE_CASE__  )
						self.add_node(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	weight
						__lowerCamelCase    					=	weight





def  __magic_name__ (   __lowerCAmelCase	:						GraphUndirectedWeighted[T]		,  )			->   tuple[dict[T, int], dict[T, T | None]]:
				__lowerCamelCase    					=	{node: maxsize for node in graph.connections}
				__lowerCamelCase    					=	{node: None for node in graph.connections}

				__lowerCamelCase    					=	MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(__lowerCAmelCase		,  __lowerCAmelCase							)

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				__lowerCamelCase    					=	priority_queue.extract_min()
				__lowerCamelCase    					=	0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
												__lowerCamelCase    					=	node

    # running prim's algorithm
				while not priority_queue.is_empty():
								__lowerCamelCase    					=	priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
																__lowerCamelCase    					=	node
				return dist, parent



