"""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
       __lowercase		:		List[str]														=							"platform"
       import jax
       import jax.numpy as jnp
       import numpy as np

       from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Any							=					PegasusConfig
    UpperCamelCase_	:					List[Any]							=					{}
    UpperCamelCase_	:					int							=					'gelu'
    def __init__(		self							:     Tuple	,      A_							:     str	,      A_							:     List[Any]=13	,      A_							:     List[str]=7	,      A_							:     Tuple=True	,      A_							:     int=False	,      A_							:     List[str]=99	,      A_							:     Tuple=32	,      A_							:     Dict=5	,      A_							:     Optional[Any]=4	,      A_							:     Dict=37	,      A_							:     List[Any]=0.1	,      A_							:     Any=0.1	,      A_							:     Optional[int]=20	,      A_							:     List[Any]=2	,      A_							:     Optional[Any]=1	,      A_							:     Any=0	,      )    ->       Union[str, Any]:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size

         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							eos_token_id
         __snake_case				=							pad_token_id
         __snake_case				=							bos_token_id
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length - 1]	,      self.vocab_size    ).clip(3	,      self.vocab_size    )
         __snake_case				=							np.expand_dims(np.array([self.eos_token_id] * self.batch_size    )	,      1    )
         __snake_case				=							np.concatenate([input_ids, eos_tensor]	,      axis=1    )

         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							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	,      )
         __snake_case				=							prepare_pegasus_inputs_dict(_a	,      _a	,      _a    )
         return config, inputs_dict
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[Any]	,      A_							:     str	,      A_							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							20
         __snake_case				=							model_class_name(_a    )

         __snake_case				=							model.encode(inputs_dict['''input_ids''']    )

         __snake_case		,	__snake_case				=							(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __snake_case				=							model.init_cache(decoder_input_ids.shape[0]	,      _a	,      _a    )
         __snake_case				=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,      dtype='''i4'''    )

         __snake_case				=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )
         __snake_case				=							model.decode(
             decoder_input_ids[:, :-1]	,      _a	,      decoder_attention_mask=_a	,      past_key_values=_a	,      decoder_position_ids=_a	,      )

         __snake_case				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype='''i4'''    )
         __snake_case				=							model.decode(
             decoder_input_ids[:, -1:]	,      _a	,      decoder_attention_mask=_a	,      past_key_values=outputs_cache.past_key_values	,      decoder_position_ids=_a	,      )

         __snake_case				=							model.decode(_a	,      _a    )

         __snake_case				=							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  lowercase					(		self							:     List[str]	,      A_							:     Tuple	,      A_							:     Dict	,      A_							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							20
         __snake_case				=							model_class_name(_a    )

         __snake_case				=							model.encode(inputs_dict['''input_ids''']    )

         __snake_case		,	__snake_case				=							(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

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

         __snake_case				=							model.init_cache(decoder_input_ids.shape[0]	,      _a	,      _a    )
         __snake_case				=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )

         __snake_case				=							model.decode(
             decoder_input_ids[:, :-1]	,      _a	,      decoder_attention_mask=_a	,      past_key_values=_a	,      decoder_position_ids=_a	,      )
         __snake_case				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype='''i4'''    )
         __snake_case				=							model.decode(
             decoder_input_ids[:, -1:]	,      _a	,      past_key_values=outputs_cache.past_key_values	,      decoder_attention_mask=_a	,      decoder_position_ids=_a	,      )

         __snake_case				=							model.decode(_a	,      _a	,      decoder_attention_mask=_a    )

         __snake_case				=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case=None,      snake_case=None,      ):
     if attention_mask is None:
          __snake_case				=							np.not_equal(lowerCAmelCase__,      config.pad_token_id).astype(np.inta)
     if decoder_attention_mask is None:
          __snake_case				=							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      _A	(						__SCREAMING_SNAKE_CASE							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    UpperCamelCase_	:					Dict							=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    UpperCamelCase_	:					Tuple							=					True
    UpperCamelCase_	:					List[str]							=					False
    UpperCamelCase_	:					int							=					False
    UpperCamelCase_	:					List[str]							=					False
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							FlaxPegasusModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=_a    )
    def  lowercase					(		self							:     List[Any]    )    ->       Tuple:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(_a	,      _a	,      _a    )
    def  lowercase					(		self							:     List[Any]    )    ->       Tuple:
         __snake_case		,	__snake_case				=							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(_a	,      _a	,      _a    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Union[str, Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(_a	,      _a    )
                   __snake_case				=							model_class(_a    )

                   @jax.jit
                   def encode_jitted(A_							:     List[Any]	,      A_							:     str=None	,      **A_							:     Union[str, Any]    ):
                        return model.encode(input_ids=_a	,      attention_mask=_a    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							encode_jitted(**_a    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							encode_jitted(**_a    ).to_tuple()

                   self.assertEqual(len(_a    )	,      len(_a    )    )
                   for jitted_output, output in zip(_a	,      _a    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    def  lowercase					(		self							:     List[str]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							model_class(_a    )
                   __snake_case				=							model.encode(inputs_dict['''input_ids''']	,      inputs_dict['''attention_mask''']    )

                   __snake_case				=							{
                       '''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(A_							:     Optional[Any]	,      A_							:     Union[str, Any]	,      A_							:     str    ):
                        return model.decode(
                            decoder_input_ids=_a	,      decoder_attention_mask=_a	,      encoder_outputs=_a	,      )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							decode_jitted(**_a    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							decode_jitted(**_a    ).to_tuple()

                   self.assertEqual(len(_a    )	,      len(_a    )    )
                   for jitted_output, output in zip(_a	,      _a    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       str:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''google/pegasus-large'''	,      from_pt=_a    )
              __snake_case				=							np.ones((1, 1)    )
              __snake_case				=							model(_a    )
              self.assertIsNotNone(_a    )







    @slow
    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''    )
         __snake_case				=							PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''    )

         __snake_case				=							[
             ''' 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!" ''',
         ]

         __snake_case				=							[
             '''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.''',
         ]

         __snake_case				=							tokenizer(_a	,      return_tensors='''np'''	,      truncation=_a	,      max_length=512	,      padding=_a    )
         __snake_case				=							model.generate(**_a	,      num_beams=2    ).sequences
         __snake_case				=							tokenizer.batch_decode(_a	,      skip_special_tokens=_a    )
         assert tgt_text == decoded
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __snake_case				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							simulated_annealing(prob, find_max=False, visualization=True)
       print(
           "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							simulated_annealing(prob, find_max=True, visualization=True)




       print(
           "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )