



import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
	    SpeechaTextaDecoder,
	    SpeechaTextaForCausalLM,
	)






@require_torch
class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						Dict						,  __UpperCAmelCase	:						List[str]=99						,  __UpperCAmelCase	:						Optional[int]=13						,  __UpperCAmelCase	:						int=16						,  __UpperCAmelCase	:						str=7						,  __UpperCAmelCase	:						Union[str, Any]=True						,  __UpperCAmelCase	:						Optional[int]=True						,  __UpperCAmelCase	:						str=True						,  __UpperCAmelCase	:						List[str]=False						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						List[Any]=2						,  __UpperCAmelCase	:						Optional[Any]=32						,  __UpperCAmelCase	:						int=4						,  __UpperCAmelCase	:						List[Any]=4						,  __UpperCAmelCase	:						List[str]=30						,  __UpperCAmelCase	:						int=0						,  __UpperCAmelCase	:						List[str]=1						,  __UpperCAmelCase	:						int=2						,  __UpperCAmelCase	:						Dict=None						,  )			-> Optional[Any]:
							UpperCAmelCase_=   parent
							UpperCAmelCase_=   batch_size
							UpperCAmelCase_=   decoder_seq_length
							# For common tests
							UpperCAmelCase_=   self.decoder_seq_length
							UpperCAmelCase_=   is_training
							UpperCAmelCase_=   use_attention_mask
							UpperCAmelCase_=   use_labels

							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   d_model
							UpperCAmelCase_=   d_model
							UpperCAmelCase_=   decoder_layers
							UpperCAmelCase_=   decoder_layers
							UpperCAmelCase_=   decoder_ffn_dim
							UpperCAmelCase_=   decoder_attention_heads
							UpperCAmelCase_=   decoder_attention_heads
							UpperCAmelCase_=   eos_token_id
							UpperCAmelCase_=   bos_token_id
							UpperCAmelCase_=   pad_token_id
							UpperCAmelCase_=   decoder_start_token_id
							UpperCAmelCase_=   use_cache
							UpperCAmelCase_=   max_position_embeddings

							UpperCAmelCase_=   None
							UpperCAmelCase_=   decoder_seq_length
							UpperCAmelCase_=   2
							UpperCAmelCase_=   1




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> List[str]:
							UpperCAmelCase_=   ids_tensor([self.batch_size, self.decoder_seq_length]						,  self.vocab_size     )

							UpperCAmelCase_=   None
							if self.use_attention_mask:
													UpperCAmelCase_=   ids_tensor([self.batch_size, self.decoder_seq_length]						,  vocab_size=2     )

							UpperCAmelCase_=   None
							if self.use_labels:
													UpperCAmelCase_=   ids_tensor([self.batch_size, self.decoder_seq_length]						,  self.vocab_size     )

							UpperCAmelCase_=   SpeechaTextaConfig(
							    vocab_size=self.vocab_size						,  d_model=self.d_model						,  decoder_layers=self.decoder_layers						,  decoder_ffn_dim=self.decoder_ffn_dim						,  decoder_attention_heads=self.decoder_attention_heads						,  eos_token_id=self.eos_token_id						,  bos_token_id=self.bos_token_id						,  use_cache=self.use_cache						,  pad_token_id=self.pad_token_id						,  decoder_start_token_id=self.decoder_start_token_id						,  max_position_embeddings=self.max_position_embeddings						,  )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    lm_labels,
							)




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						List[str]						,  )			-> Union[str, Any]:
							UpperCAmelCase_=   True
							UpperCAmelCase_=   SpeechaTextaDecoder(config=__UpperCAmelCase     ).to(__UpperCAmelCase     ).eval()
							UpperCAmelCase_=   input_ids[:2]

							input_ids[input_ids == 0] += 1
							# first forward pass
							UpperCAmelCase_=   model(__UpperCAmelCase						,  use_cache=__UpperCAmelCase     )
							UpperCAmelCase_=   model(__UpperCAmelCase     )
							UpperCAmelCase_=   model(__UpperCAmelCase						,  use_cache=__UpperCAmelCase     )

							self.parent.assertTrue(len(__UpperCAmelCase     ) == len(__UpperCAmelCase     )     )
							self.parent.assertTrue(len(__UpperCAmelCase     ) == len(__UpperCAmelCase     ) + 1     )

							UpperCAmelCase_=   outputs["""past_key_values"""]

							# create hypothetical next token and extent to next_input_ids
							UpperCAmelCase_=   ids_tensor((2, 1)						,  config.vocab_size - 1     ) + 1

							# append to next input_ids and
							UpperCAmelCase_=   torch.cat([input_ids, next_tokens]						,  dim=-1     )

							print(__UpperCAmelCase     )

							UpperCAmelCase_=   model(__UpperCAmelCase     )["""last_hidden_state"""]
							UpperCAmelCase_=   model(__UpperCAmelCase						,  past_key_values=__UpperCAmelCase     )["""last_hidden_state"""]

							# select random slice
							UpperCAmelCase_=   ids_tensor((1,)						,  output_from_past.shape[-1]     ).item()
							UpperCAmelCase_=   output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
							UpperCAmelCase_=   output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							assert torch.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> int:
							UpperCAmelCase_=   self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   
							)=   config_and_inputs

							UpperCAmelCase_=   {
							    """input_ids""": input_ids,
							    """attention_mask""": attention_mask,
							}
							return config, inputs_dict






@require_torch
class 		lowercase						( snake_case__ ,    snake_case__ ,    snake_case__ ,    unittest.TestCase):






	"""simple docstring"""


	a__      :					int		   =							(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
	a__      :					List[Any]		   =							(SpeechaTextaForCausalLM,) if is_torch_available() else ()
	a__      :					str		   =							{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
	a__      :					List[str]		   =							True
	a__      :					List[str]		   =							False




	def   _SCREAMING_SNAKE_CASE		(	self	:						str						,  )			-> List[Any]:
							UpperCAmelCase_=   SpeechaTextaStandaloneDecoderModelTester(self						,  is_training=__UpperCAmelCase     )
							UpperCAmelCase_=   ConfigTester(self						,  config_class=__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> str:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Optional[Any]:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> List[str]:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> str:
							self.config_tester.run_common_tests()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> int:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past(*__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Any:
							return









from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__A				    =						{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}



def        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))




