



'''simple docstring'''




import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						lowerCamelCase							:Dict				    =					'''platform'''
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		Tuple					=					PegasusConfig
	__SCREAMING_SNAKE_CASE   :		int					=					{}
	__SCREAMING_SNAKE_CASE   :		str					=					'gelu'


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=False       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=20       ,      lowercase=2       ,      lowercase=1       ,      lowercase=0       ,      ):
				A_	:						List[str]	=     parent
				A_	:						Union[str, Any]	=     batch_size
				A_	:						Optional[int]	=     seq_length
				A_	:						Dict	=     is_training
				A_	:						Dict	=     use_labels
				A_	:						Union[str, Any]	=     vocab_size
				A_	:						str	=     hidden_size
				A_	:						List[Any]	=     num_hidden_layers
				A_	:						Optional[int]	=     num_attention_heads
				A_	:						List[Any]	=     intermediate_size

				A_	:						List[str]	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Dict	=     max_position_embeddings
				A_	:						Dict	=     eos_token_id
				A_	:						List[Any]	=     pad_token_id
				A_	:						Dict	=     bos_token_id


	def     _a	(self							):
				A_	:						Optional[Any]	=     ids_tensor([self.batch_size, self.seq_length - 1]       ,      self.vocab_size							).clip(3       ,      self.vocab_size							)
				A_	:						List[str]	=     np.expand_dims(np.array([self.eos_token_id] * self.batch_size							)       ,      1							)
				A_	:						Union[str, Any]	=     np.concatenate([input_ids, eos_tensor]       ,      axis=1							)

				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						int	=     self.config_cls(
				    vocab_size=self.vocab_size       ,      d_model=self.hidden_size       ,      encoder_layers=self.num_hidden_layers       ,      decoder_layers=self.num_hidden_layers       ,      encoder_attention_heads=self.num_attention_heads       ,      decoder_attention_heads=self.num_attention_heads       ,      encoder_ffn_dim=self.intermediate_size       ,      decoder_ffn_dim=self.intermediate_size       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      eos_token_ids=[2]       ,      bos_token_id=self.bos_token_id       ,      pad_token_id=self.pad_token_id       ,      decoder_start_token_id=self.pad_token_id       ,      **self.config_updates       ,      )
				A_	:						List[str]	=     prepare_pegasus_inputs_dict(__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							)
				return config, inputs_dict


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Dict	=     20
				A_	:						List[str]	=     model_class_name(__lowerCAmelCase							)

				A_	:						Dict	=     model.encode(inputs_dict["""input_ids"""]							)

				A_,    A_	:						Any	=     (
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

				A_	:						Dict	=     model.init_cache(decoder_input_ids.shape[0]       ,      __lowerCAmelCase       ,      __lowerCAmelCase							)
				A_	:						List[Any]	=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,      dtype="""i4"""							)

				A_	:						Any	=     jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]       ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,      )
				A_	:						Dict	=     model.decode(
				    decoder_input_ids[:, :-1]       ,      __lowerCAmelCase       ,      decoder_attention_mask=__lowerCAmelCase       ,      past_key_values=__lowerCAmelCase       ,      decoder_position_ids=__lowerCAmelCase       ,      )

				A_	:						List[Any]	=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,      dtype="""i4"""							)
				A_	:						Any	=     model.decode(
				    decoder_input_ids[:, -1:]       ,      __lowerCAmelCase       ,      decoder_attention_mask=__lowerCAmelCase       ,      past_key_values=outputs_cache.past_key_values       ,      decoder_position_ids=__lowerCAmelCase       ,      )

				A_	:						Any	=     model.decode(__lowerCAmelCase       ,      __lowerCAmelCase							)

				A_	:						Union[str, Any]	=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
				self.parent.assertTrue(diff < 1E-3       ,      msg=F'Max diff is {diff}'							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Optional[int]	=     20
				A_	:						Union[str, Any]	=     model_class_name(__lowerCAmelCase							)

				A_	:						Union[str, Any]	=     model.encode(inputs_dict["""input_ids"""]							)

				A_,    A_	:						Dict	=     (
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

				A_	:						str	=     jnp.concatenate(
				    [
				        decoder_attention_mask,
				        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
				    ]       ,      axis=-1       ,      )

				A_	:						Optional[int]	=     model.init_cache(decoder_input_ids.shape[0]       ,      __lowerCAmelCase       ,      __lowerCAmelCase							)
				A_	:						Any	=     jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]       ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,      )

				A_	:						List[str]	=     model.decode(
				    decoder_input_ids[:, :-1]       ,      __lowerCAmelCase       ,      decoder_attention_mask=__lowerCAmelCase       ,      past_key_values=__lowerCAmelCase       ,      decoder_position_ids=__lowerCAmelCase       ,      )
				A_	:						List[Any]	=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,      dtype="""i4"""							)
				A_	:						Tuple	=     model.decode(
				    decoder_input_ids[:, -1:]       ,      __lowerCAmelCase       ,      past_key_values=outputs_cache.past_key_values       ,      decoder_attention_mask=__lowerCAmelCase       ,      decoder_position_ids=__lowerCAmelCase       ,      )

				A_	:						Optional[int]	=     model.decode(__lowerCAmelCase       ,      __lowerCAmelCase       ,      decoder_attention_mask=__lowerCAmelCase							)

				A_	:						Dict	=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
				self.parent.assertTrue(diff < 1E-3       ,      msg=F'Max diff is {diff}'							)






def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      ):
			'''simple docstring'''





			if attention_mask is None:
						A_	:						Any	=     np.not_equal(lowerCamelCase__						,      config.pad_token_id    ).astype(np.inta    )
			if decoder_attention_mask is None:
						A_	:						List[str]	=     np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape						,      dtype=np.inta    ),
						        np.not_equal(decoder_input_ids[:, 1:]						,      config.pad_token_id    ).astype(np.inta    ),
						    ]						,      axis=-1						,      )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}







@require_flax
class 	_lowerCAmelCase							(			lowerCAmelCase_      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Dict					=					(
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					True
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Dict					=					False
	__SCREAMING_SNAKE_CASE   :		str					=					False


	def     _a	(self							):
				A_	:						List[str]	=     FlaxPegasusModelTester(self							)
				A_	:						Union[str, Any]	=     ConfigTester(self       ,      config_class=__lowerCAmelCase							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							)


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							)


	def     _a	(self							):
				A_,    A_	:						Dict	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										A_	:						Union[str, Any]	=     self._prepare_for_class(__lowerCAmelCase       ,      __lowerCAmelCase							)
										A_	:						List[Any]	=     model_class(__lowerCAmelCase							)

										@jax.jit
										def encode_jitted(lowercase       ,      lowercase=None       ,      **lowercase							):
													return model.encode(input_ids=__lowerCAmelCase       ,      attention_mask=__lowerCAmelCase							)

										with self.subTest("""JIT Enabled"""							):
													A_	:						Tuple	=     encode_jitted(**__lowerCAmelCase							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
																A_	:						Union[str, Any]	=     encode_jitted(**__lowerCAmelCase							).to_tuple()

										self.assertEqual(len(__lowerCAmelCase							)       ,      len(__lowerCAmelCase							)							)
										for jitted_output, output in zip(__lowerCAmelCase       ,      __lowerCAmelCase							):
													self.assertEqual(jitted_output.shape       ,      output.shape							)


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										A_	:						int	=     model_class(__lowerCAmelCase							)
										A_	:						Optional[int]	=     model.encode(inputs_dict["""input_ids"""]       ,      inputs_dict["""attention_mask"""]							)

										A_	:						Optional[int]	=     {
										    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
										    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
										    """encoder_outputs""": encoder_outputs,
										}

										@jax.jit
										def decode_jitted(lowercase       ,      lowercase       ,      lowercase							):
													return model.decode(
													    decoder_input_ids=__lowerCAmelCase       ,      decoder_attention_mask=__lowerCAmelCase       ,      encoder_outputs=__lowerCAmelCase       ,      )

										with self.subTest("""JIT Enabled"""							):
													A_	:						Dict	=     decode_jitted(**__lowerCAmelCase							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
																A_	:						Union[str, Any]	=     decode_jitted(**__lowerCAmelCase							).to_tuple()

										self.assertEqual(len(__lowerCAmelCase							)       ,      len(__lowerCAmelCase							)							)
										for jitted_output, output in zip(__lowerCAmelCase       ,      __lowerCAmelCase							):
													self.assertEqual(jitted_output.shape       ,      output.shape							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						List[str]	=     model_class_name.from_pretrained("""google/pegasus-large"""       ,      from_pt=__lowerCAmelCase							)
							A_	:						str	=     np.ones((1, 1)							)
							A_	:						Optional[Any]	=     model(__lowerCAmelCase							)
							self.assertIsNotNone(__lowerCAmelCase							)


	@slow
	def     _a	(self							):
				A_	:						str	=     FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""							)
				A_	:						int	=     PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""							)

				A_	:						Optional[Any]	=     [
				    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
				    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
				]

				A_	:						List[str]	=     [
				    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
				    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
				]

				A_	:						str	=     tokenizer(__lowerCAmelCase       ,      return_tensors="""np"""       ,      truncation=__lowerCAmelCase       ,      max_length=512       ,      padding=__lowerCAmelCase							)
				A_	:						int	=     model.generate(**__lowerCAmelCase       ,      num_beams=2							).sequences
				A_	:						List[Any]	=     tokenizer.batch_decode(__lowerCAmelCase       ,      skip_special_tokens=__lowerCAmelCase							)
				assert tgt_text == decoded
"""simple docstring"""


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

  while queue:
    _UpperCAmelCase         =					queue.pop(0						)
    for ind in range(len(graph[u]						)						):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase						)
        _UpperCAmelCase         =					True
        _UpperCAmelCase         =					u

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

    while s != source:
      # Find the minimum value in select path
      _UpperCAmelCase         =					min(lowercase      ,graph[parent[s]][s]						)
      _UpperCAmelCase         =					parent[s]

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
