


"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
       UpperCAmelCase__					="platform"

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )


def lowerCAmelCase_   (					UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			int=None					, UpperCamelCase__						:			List[Any]=None					, UpperCamelCase__						:			List[Any]=None					, UpperCamelCase__						:			Any=None					, UpperCamelCase__						:			List[str]=None					, UpperCamelCase__						:			Optional[int]=None					, ):






 """simple docstring"""







 if attention_mask is None:
  __lowercase						=						np.where(input_ids != config.pad_token_id					, 1					, 0			)
 if decoder_attention_mask is None:
  __lowercase						=						np.where(decoder_input_ids != config.pad_token_id					, 1					, 0			)
 if head_mask is None:
  __lowercase						=						np.ones((config.encoder_layers, config.encoder_attention_heads)			)
 if decoder_head_mask is None:
  __lowercase						=						np.ones((config.decoder_layers, config.decoder_attention_heads)			)
 if cross_attn_head_mask is None:
  __lowercase						=						np.ones((config.decoder_layers, config.decoder_attention_heads)			)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }





class  lowerCamelCase__    :
      def __init__(		self						:						Union[str, Any]   ,							A_						:						Union[str, Any]   ,							A_						:						Union[str, Any]=1_3   ,							A_						:						Optional[int]=7   ,							A_						:						List[str]=True   ,							A_						:						str=False   ,							A_						:						List[str]=9_9   ,							A_						:						Any=1_6   ,							A_						:						Dict=2   ,							A_						:						Any=4   ,							A_						:						List[str]=4   ,							A_						:						Dict="gelu"   ,							A_						:						Tuple=0.1   ,							A_						:						int=0.1   ,							A_						:						List[Any]=3_2   ,							A_						:						int=2   ,							A_						:						Union[str, Any]=1   ,							A_						:						List[Any]=0   ,							A_						:						str=0.02   ,							):






       '''simple docstring'''


       __lowercase						=						parent
       __lowercase						=						batch_size
       __lowercase						=						seq_length
       __lowercase						=						is_training
       __lowercase						=						use_labels
       __lowercase						=						vocab_size
       __lowercase						=						hidden_size
       __lowercase						=						num_hidden_layers
       __lowercase						=						num_attention_heads
       __lowercase						=						intermediate_size
       __lowercase						=						hidden_act
       __lowercase						=						hidden_dropout_prob
       __lowercase						=						attention_probs_dropout_prob
       __lowercase						=						max_position_embeddings
       __lowercase						=						eos_token_id
       __lowercase						=						pad_token_id
       __lowercase						=						bos_token_id
       __lowercase						=						initializer_range
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,							self.vocab_size				)   ,							3   ,							self.vocab_size				)
       __lowercase						=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,							dtype=np.intaa				))   ,							-1				)

       __lowercase						=						shift_tokens_right(A_   ,							1   ,							2				)

       __lowercase						=						BlenderbotConfig(
           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_id=self.eos_token_id   ,							bos_token_id=self.bos_token_id   ,							pad_token_id=self.pad_token_id   ,							initializer_range=self.initializer_range   ,							use_cache=A_   ,							)
       __lowercase						=						prepare_blenderbot_inputs_dict(A_   ,							A_   ,							A_				)
       return config, inputs_dict
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.prepare_config_and_inputs()
       return config, inputs_dict
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						2_0
       __lowercase						=						model_class_name(A_				)

       __lowercase						=						model.encode(inputs_dict["""input_ids"""]				)

       __lowercase				,		__lowercase						=						(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

       __lowercase						=						model.init_cache(decoder_input_ids.shape[0]   ,							A_   ,							A_				)
       __lowercase						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,							dtype="""i4"""				)

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

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

       __lowercase						=						model.decode(A_   ,							A_				)

       __lowercase						=						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						:						int   ,							A_						:						str   ,							A_						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						2_0
       __lowercase						=						model_class_name(A_				)

       __lowercase						=						model.encode(inputs_dict["""input_ids"""]				)

       __lowercase				,		__lowercase						=						(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

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

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

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

       __lowercase						=						model.decode(A_   ,							A_   ,							decoder_attention_mask=A_				)

       __lowercase						=						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}'''				)






@require_flax
class  lowerCamelCase__    (		unittest.TestCase     ):
      a			:  Tuple           =		99
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       __lowercase						=						np.array(
           [
               [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
               [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
               [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
               [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
               [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
               [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
               [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
               [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
               [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
               [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
               [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
               [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
               [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
           ]   ,							dtype=np.intaa   ,							)

       __lowercase						=						input_ids.shape[0]
       __lowercase						=						BlenderbotConfig(
           vocab_size=self.vocab_size   ,							d_model=2_4   ,							encoder_layers=2   ,							decoder_layers=2   ,							encoder_attention_heads=2   ,							decoder_attention_heads=2   ,							encoder_ffn_dim=3_2   ,							decoder_ffn_dim=3_2   ,							max_position_embeddings=4_8   ,							eos_token_id=2   ,							pad_token_id=1   ,							bos_token_id=0   ,							)
       return config, input_ids, batch_size
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase				,		__lowercase				,		__lowercase						=						self._get_config_and_data()
       __lowercase						=						FlaxBlenderbotForConditionalGeneration(A_				)
       __lowercase						=						lm_model(input_ids=A_				)
       __lowercase						=						(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						BlenderbotConfig(
           vocab_size=self.vocab_size   ,							d_model=1_4   ,							encoder_layers=2   ,							decoder_layers=2   ,							encoder_attention_heads=2   ,							decoder_attention_heads=2   ,							encoder_ffn_dim=8   ,							decoder_ffn_dim=8   ,							max_position_embeddings=4_8   ,							)
       __lowercase						=						FlaxBlenderbotForConditionalGeneration(A_				)
       __lowercase						=						np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]   ,							dtype=np.intaa				)
       __lowercase						=						np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]   ,							dtype=np.intaa				)
       __lowercase						=						lm_model(input_ids=A_   ,							decoder_input_ids=A_				)
       __lowercase						=						(*summary.shape, config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       __lowercase						=						np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]   ,							dtype=np.intaa				)
       __lowercase						=						shift_tokens_right(A_   ,							1   ,							2				)
       __lowercase						=						np.equal(A_   ,							1				).astype(np.floataa				).sum()
       __lowercase						=						np.equal(A_   ,							1				).astype(np.floataa				).sum()
       self.assertEqual(shifted.shape   ,							input_ids.shape				)
       self.assertEqual(A_   ,							n_pad_before - 1				)
       self.assertTrue(np.equal(shifted[:, 0]   ,							2				).all()				)






@require_flax
class  lowerCamelCase__    (		_a      , unittest.TestCase      , _a     ):
      a			:  Optional[Any]           =		True
      a			:  List[str]           =		(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      a			:  List[Any]           =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						FlaxBlenderbotModelTester(self				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__				):
         __lowercase						=						self._prepare_for_class(A_   ,							A_				)
         __lowercase						=						model_class(A_				)

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

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

         with self.subTest("""JIT Disabled"""				):
          with jax.disable_jit():
           __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs_for_common()

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

         __lowercase						=						{
             """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_						:						Union[str, Any]   ,							A_						:						List[Any]   ,							A_						:						Dict				):
          return model.decode(
              decoder_input_ids=A_   ,							decoder_attention_mask=A_   ,							encoder_outputs=A_   ,							)

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

         with self.subTest("""JIT Disabled"""				):
          with jax.disable_jit():
           __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       for model_class_name in self.all_model_classes:
        __lowercase						=						model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""				)
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        __lowercase						=						np.ones((1, 1)				) * model.config.eos_token_id
        __lowercase						=						model(A_				)
        self.assertIsNotNone(A_				)






      @unittest.skipUnless(jax_device != """cpu"""   ,							"""3B test too slow on CPU."""				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						{"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
       __lowercase						=						{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

       __lowercase						=						FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""   ,							from_pt=A_				)
       __lowercase						=						BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""				)

       __lowercase						=						["""Sam"""]
       __lowercase						=						tokenizer(A_   ,							return_tensors="""jax"""				)

       __lowercase						=						model.generate(**A_   ,							**A_				)
       __lowercase						=						"""Sam is a great name. It means \"sun\" in Gaelic."""

       __lowercase						=						tokenizer.batch_decode(A_   ,							**A_				)
       assert generated_txt[0].strip() == tgt_text




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
  if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
   return True
 return False


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

 for _ in range(vertex_count - 1			):
  for j in range(UpperCamelCase__			):
   __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

   if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
    __lowercase						=						distance[u] + w

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

       for i in range(E):
              print("Edge ", i + 1)
              UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=(
                  int(x)
                  for x in input("Enter source, destination, weight: ").strip().split(" ")
              )
              UpperCAmelCase__					={"src": src, "dst": dest, "weight": weight}

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
