


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
   _A		:				Union[str, Any]									=       '''platform'''
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class        a					:
     UpperCAmelCase__ :  Optional[Any]						      =	PegasusConfig
     UpperCAmelCase__ :  Optional[int]						      =	{}
     UpperCAmelCase__ :  int						      =	"gelu"





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

        __lowerCamelCase:				Union[str, Any]     =	hidden_dropout_prob
        __lowerCamelCase:				List[Any]     =	attention_probs_dropout_prob
        __lowerCamelCase:				Any     =	max_position_embeddings
        __lowerCamelCase:				Optional[int]     =	eos_token_id
        __lowerCamelCase:				List[Any]     =	pad_token_id
        __lowerCamelCase:				str     =	bos_token_id





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

        __lowerCamelCase:				int     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Optional[Any]     =	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    ,	)
        __lowerCamelCase:				Dict     =	prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        return config, inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				int     =	20
        __lowerCamelCase:				Dict     =	model_class_name(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	model.encode(inputs_dict["""input_ids"""]							)

        __lowerCamelCase:				Optional[int]     =	(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        __lowerCamelCase:				Tuple     =	model.init_cache(decoder_input_ids.shape[0]    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,	dtype="""i4"""							)

        __lowerCamelCase:				Union[str, Any]     =	jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]    ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,	)
        __lowerCamelCase:				List[str]     =	model.decode(
            decoder_input_ids[:, :-1]    ,	SCREAMING_SNAKE_CASE_    ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=SCREAMING_SNAKE_CASE_    ,	decoder_position_ids=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				str     =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,	dtype="""i4"""							)
        __lowerCamelCase:				Optional[Any]     =	model.decode(
            decoder_input_ids[:, -1:]    ,	SCREAMING_SNAKE_CASE_    ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=outputs_cache.past_key_values    ,	decoder_position_ids=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				Any     =	model.decode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				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  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				int							):
        __lowerCamelCase:				int     =	20
        __lowerCamelCase:				Optional[Any]     =	model_class_name(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	model.encode(inputs_dict["""input_ids"""]							)

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

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

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

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

        __lowerCamelCase:				Any     =	model.decode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	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     __lowerCAmelCase						(							snake_case	:    List[str]						,      snake_case	:    Any						,      snake_case	:    Optional[int]						,      snake_case	:    Any=None						,      snake_case	:    Any=None						,      )       ->							str:
   if attention_mask is None:
      __lowerCamelCase:				str     =	np.not_equal(snake_case						,      config.pad_token_id ).astype(np.inta )
   if decoder_attention_mask is None:
      __lowerCamelCase:				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        a					(			_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  int						      =	(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     UpperCAmelCase__ :  List[str]						      =	(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     UpperCAmelCase__ :  Union[str, Any]						      =	True
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  List[Any]						      =	False
     UpperCAmelCase__ :  List[Any]						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Any     =	FlaxPegasusModelTester(self							)
        __lowerCamelCase:				str     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[Any]     =	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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Optional[Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
              __lowerCamelCase:				Tuple     =	self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Optional[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)

              @jax.jit
              def encode_jitted(SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	**SCREAMING_SNAKE_CASE_					:				Tuple							):
                 return model.encode(input_ids=SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)

              with self.subTest("""JIT Enabled"""							):
                 __lowerCamelCase:				Union[str, Any]     =	encode_jitted(**SCREAMING_SNAKE_CASE_							).to_tuple()

              with self.subTest("""JIT Disabled"""							):
                 with jax.disable_jit():
                    __lowerCamelCase:				Optional[int]     =	encode_jitted(**SCREAMING_SNAKE_CASE_							).to_tuple()

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
              __lowerCamelCase:				str     =	model_class(SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				int     =	model.encode(inputs_dict["""input_ids"""]    ,	inputs_dict["""attention_mask"""]							)

              __lowerCamelCase:				Optional[Any]     =	{
                  """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(SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
                 return model.decode(
                     decoder_input_ids=SCREAMING_SNAKE_CASE_    ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_    ,	encoder_outputs=SCREAMING_SNAKE_CASE_    ,	)

              with self.subTest("""JIT Enabled"""							):
                 __lowerCamelCase:				Any     =	decode_jitted(**SCREAMING_SNAKE_CASE_							).to_tuple()

              with self.subTest("""JIT Disabled"""							):
                 with jax.disable_jit():
                    __lowerCamelCase:				Optional[Any]     =	decode_jitted(**SCREAMING_SNAKE_CASE_							).to_tuple()

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





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:				Tuple     =	model_class_name.from_pretrained("""google/pegasus-large"""    ,	from_pt=SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				List[str]     =	np.ones((1, 1)							)
           __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				int     =	FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""							)
        __lowerCamelCase:				Optional[int]     =	PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""							)

        __lowerCamelCase:				List[str]     =	[
            """ 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!\" """,
        ]

        __lowerCamelCase:				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.""",
        ]

        __lowerCamelCase:				Any     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	return_tensors="""np"""    ,	truncation=SCREAMING_SNAKE_CASE_    ,	max_length=512    ,	padding=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	model.generate(**SCREAMING_SNAKE_CASE_    ,	num_beams=2							).sequences
        __lowerCamelCase:				Optional[Any]     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    ,	skip_special_tokens=SCREAMING_SNAKE_CASE_							)
        assert tgt_text == decoded







from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


