




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
 lowerCamelCase    :		Any			   =							"platform"

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[Any]     ,							lowercase     :	List[str]=None     ,							lowercase     :	List[str]=None     ,							lowercase     :	str=None     ,							lowercase     :	Tuple=None     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	Union[str, Any]=None     ,							):


      '''simple docstring'''



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



 '''simple docstring'''


 def __init__(    self :  List[Any]						,							A_ :  Tuple						,							A_ :  List[str]=13						,							A_ :  Optional[Any]=7						,							A_ :  int=True						,							A_ :  List[Any]=False						,							A_ :  str=99						,							A_ :  str=16						,							A_ :  Dict=2						,							A_ :  List[str]=4						,							A_ :  Optional[int]=4						,							A_ :  Optional[int]="gelu"						,							A_ :  List[str]=0.1						,							A_ :  Tuple=0.1						,							A_ :  List[Any]=32						,							A_ :  Dict=2						,							A_ :  Dict=1						,							A_ :  Tuple=0						,							A_ :  str=0.02						,							)       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   seq_length
       lowerCamelCase_							=   is_training
       lowerCamelCase_							=   use_labels
       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   eos_token_id
       lowerCamelCase_							=   pad_token_id
       lowerCamelCase_							=   bos_token_id
       lowerCamelCase_							=   initializer_range

 def     a__			(    self :  Tuple  )       ->     Union[str, Any]:

       """simple docstring"""






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

       lowerCamelCase_							=   shift_tokens_right(A_						,							1						,							2  )

       lowerCamelCase_							=   BlenderbotSmallConfig(
           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_						,							)
       lowerCamelCase_							=   prepare_blenderbot_inputs_dict(A_						,							A_						,							A_  )
       return config, inputs_dict

 def     a__			(    self :  Optional[Any]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.prepare_config_and_inputs()
       return config, inputs_dict

 def     a__			(    self :  int						,							A_ :  Optional[int]						,							A_ :  str						,							A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   20
       lowerCamelCase_							=   model_class_name(A_  )

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

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

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

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

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

       lowerCamelCase_							=   model.decode(A_						,							A_  )

       lowerCamelCase_							=   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 :  Optional[Any]						,							A_ :  Dict						,							A_ :  Tuple						,							A_ :  Union[str, Any]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   20
       lowerCamelCase_							=   model_class_name(A_  )

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

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

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

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

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

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

       lowerCamelCase_							=   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   A(						unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						99

 def     a__			(    self :  int  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]						,							dtype=np.intaa						,							)

       lowerCamelCase_							=   input_ids.shape[0]
       lowerCamelCase_							=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size						,							d_model=24						,							encoder_layers=2						,							decoder_layers=2						,							encoder_attention_heads=2						,							decoder_attention_heads=2						,							encoder_ffn_dim=32						,							decoder_ffn_dim=32						,							max_position_embeddings=48						,							eos_token_id=2						,							pad_token_id=1						,							bos_token_id=0						,							)
       return config, input_ids, batch_size

 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   self._get_config_and_data()
       lowerCamelCase_							=   FlaxBlenderbotSmallForConditionalGeneration(A_  )
       lowerCamelCase_							=   lm_model(input_ids=A_  )
       lowerCamelCase_							=   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size						,							d_model=14						,							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=48						,							)
       lowerCamelCase_							=   FlaxBlenderbotSmallForConditionalGeneration(A_  )
       lowerCamelCase_							=   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   lm_model(input_ids=A_						,							decoder_input_ids=A_  )
       lowerCamelCase_							=   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,							A_  )





 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   shift_tokens_right(A_						,							1						,							2  )
       lowerCamelCase_							=   np.equal(A_						,							1  ).astype(np.floataa  ).sum()
       lowerCamelCase_							=   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   A(						UpperCamelCase							,				unittest.TestCase							,				UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						True
 UpperCamelCase						       =						(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 UpperCamelCase						       =						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

 def     a__			(    self :  Dict  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   FlaxBlenderbotSmallModelTester(self  )

 def     a__			(    self :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   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     a__			(    self :  Tuple  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   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     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

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

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

                   with self.subTest('JIT Enabled'  ):
                         lowerCamelCase_							=   encode_jitted(**A_  ).to_tuple()

                   with self.subTest('JIT Disabled'  ):
                         with jax.disable_jit():
                               lowerCamelCase_							=   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     a__			(    self :  Union[str, Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase_							=   {
                       '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_ :  Optional[Any]						,							A_ :  Optional[Any]  ):
                         return model.decode(
                             decoder_input_ids=A_						,							decoder_attention_mask=A_						,							encoder_outputs=A_						,							)

                   with self.subTest('JIT Enabled'  ):
                         lowerCamelCase_							=   decode_jitted(**A_  ).to_tuple()

                   with self.subTest('JIT Disabled'  ):
                         with jax.disable_jit():
                               lowerCamelCase_							=   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     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_							=   model_class_name.from_pretrained('facebook/blenderbot_small-90M'  )
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             lowerCamelCase_							=   np.ones((1, 1)  ) * model.config.eos_token_id
             lowerCamelCase_							=   model(A_  )
             self.assertIsNotNone(A_  )









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



