






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
    _SCREAMING_SNAKE_CASE   =  "platform"

    import jax
    import jax.numpy as jnp

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





def        _snake_case   (_snake_case				:       List[str]      ,    _snake_case				:       List[Any]      ,    _snake_case				:       Union[str, Any]=None      ,    _snake_case				:       List[Any]=None      ,    _snake_case				:       Any=None      ,    _snake_case				:       str=None      ,    _snake_case				:       int=None      ,    _snake_case				:       str=None      ,    )					->	Tuple:
   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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:Dict,      snake_case	:Tuple=13,      snake_case	:Any=7,      snake_case	:Any=True,      snake_case	:int=False,      snake_case	:int=99,      snake_case	:Union[str, Any]=16,      snake_case	:Optional[Any]=2,      snake_case	:Optional[int]=4,      snake_case	:str=4,      snake_case	:str="gelu",      snake_case	:Optional[int]=0.1,      snake_case	:Dict=0.1,      snake_case	:List[str]=32,      snake_case	:str=2,      snake_case	:Any=1,      snake_case	:List[Any]=0,      snake_case	:Dict=0.0_2,      ):



          """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 				UpperCamelCase__						(				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(snake_case,      1,      2)

          _lowercase              =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=snake_case,      )
          _lowercase              =prepare_blenderbot_inputs_dict(snake_case,      snake_case,      snake_case)
          return config, inputs_dict





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Any,      snake_case	:Any,      snake_case	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =20
          _lowercase              =model_class_name(snake_case)

          _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],      snake_case,      snake_case)
          _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],      snake_case,      decoder_attention_mask=snake_case,      past_key_values=snake_case,      decoder_position_ids=snake_case,      )

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

          _lowercase              =model.decode(snake_case,      snake_case)

          _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 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:List[Any],      snake_case	:List[str],      snake_case	:Optional[Any]):



          """simple docstring"""

          _lowercase              =20
          _lowercase              =model_class_name(snake_case)

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

          _lowercase              =model.decode(snake_case,      snake_case,      decoder_attention_mask=snake_case)

          _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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=9_9





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =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,      )

          _lowercase              =input_ids.shape[0]
          _lowercase              =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 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],      dtype=np.intaa)
          _lowercase              =shift_tokens_right(snake_case,      1,      2)
          _lowercase              =np.equal(snake_case,      1).astype(np.floataa).sum()
          _lowercase              =np.equal(snake_case,      1).astype(np.floataa).sum()
          self.assertEqual(shifted.shape,      input_ids.shape)
          self.assertEqual(snake_case,      n_pad_before - 1)
          self.assertTrue(np.equal(shifted[:, 0],      2).all())






@require_flax
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase  ,						_a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=True
       __lowerCAmelCase    :		List[str]					=(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       __lowerCAmelCase    :		Tuple					=(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =FlaxBlenderbotSmallModelTester(self)





       def 				UpperCamelCase__						(				self	:List[str]):



          """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(snake_case,      snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str]):



          """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(snake_case,      snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Tuple):



          """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(snake_case,      snake_case)
                _lowercase              =model_class(snake_case)

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

                with self.subTest('JIT Enabled'):
                   _lowercase              =encode_jitted(**snake_case).to_tuple()

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

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





       def 				UpperCamelCase__						(				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(snake_case)
                _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(snake_case	:int,      snake_case	:Dict,      snake_case	:Dict):
                   return model.decode(
                       decoder_input_ids=snake_case,      decoder_attention_mask=snake_case,      encoder_outputs=snake_case,      )

                with self.subTest('JIT Enabled'):
                   _lowercase              =decode_jitted(**snake_case).to_tuple()

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

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





       @slow
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

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










# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          if not self.directed:  # For undirected graphs
             # if both source vertex and destination vertex are both present in the
             # adjacency list, add destination vertex to source vertex list of adjacent
             # vertices and add source vertex to destination vertex list of adjacent
             # vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination vertex
             # to source vertex list of adjacent vertices, then create a new vertex with
             # destination vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[source_vertex]
             # if only destination vertex is present in adjacency list, add source vertex
             # to destination vertex list of adjacent vertices, then create a new vertex
             # with source vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(snake_case)
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and assign a list
             # containing the destination vertex as it's first adjacent vertex also
             # create a new vertex with destination vertex as key and assign a list
             # containing the source vertex as it's first adjacent vertex.
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[source_vertex]
          else:  # For directed graphs
             # if both source vertex and destination vertex are present in adjacency
             # list, add destination vertex to source vertex list of adjacent vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination
             # vertex to source vertex list of adjacent vertices and create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[]
             # if only destination vertex is present in adjacency list, create a new
             # vertex with source vertex as key and assign a list containing destination
             # vertex as first adjacent vertex
             elif destination_vertex in self.adj_list:
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and a list containing
             # destination vertex as it's first adjacent vertex. Then create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[]

          return self





       def __repr__(				self	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


