



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         =			'''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					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  )     ->     Union[str, Any]:






      '''simple docstring'''







      if attention_mask is None:
            UpperCAmelCase_  :	Union[str, Any]				      =		np.where(input_ids != config.pad_token_id					,  1					,  0    )
      if decoder_attention_mask is None:
            UpperCAmelCase_  :	Tuple				      =		np.where(decoder_input_ids != config.pad_token_id					,  1					,  0    )
      if head_mask is None:
            UpperCAmelCase_  :	Union[str, Any]				      =		np.ones((config.encoder_layers, config.encoder_attention_heads)    )
      if decoder_head_mask is None:
            UpperCAmelCase_  :	Tuple				      =		np.ones((config.decoder_layers, config.decoder_attention_heads)    )
      if cross_attn_head_mask is None:
            UpperCAmelCase_  :	List[str]				      =		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   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=True     ,lowercase=False     ,lowercase=99     ,lowercase=16     ,lowercase=2     ,lowercase=4     ,lowercase=4     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=32     ,lowercase=2     ,lowercase=1     ,lowercase=0     ,lowercase=0.02     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		parent
             UpperCAmelCase_  :	Dict				      =		batch_size
             UpperCAmelCase_  :	Tuple				      =		seq_length
             UpperCAmelCase_  :	Any				      =		is_training
             UpperCAmelCase_  :	Optional[int]				      =		use_labels
             UpperCAmelCase_  :	Optional[Any]				      =		vocab_size
             UpperCAmelCase_  :	Optional[int]				      =		hidden_size
             UpperCAmelCase_  :	Optional[int]				      =		num_hidden_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		num_attention_heads
             UpperCAmelCase_  :	str				      =		intermediate_size
             UpperCAmelCase_  :	Any				      =		hidden_act
             UpperCAmelCase_  :	int				      =		hidden_dropout_prob
             UpperCAmelCase_  :	str				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Tuple				      =		max_position_embeddings
             UpperCAmelCase_  :	Dict				      =		eos_token_id
             UpperCAmelCase_  :	List[str]				      =		pad_token_id
             UpperCAmelCase_  :	int				      =		bos_token_id
             UpperCAmelCase_  :	Any				      =		initializer_range



       def  A_   (     self):

             """simple docstring"""





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

             UpperCAmelCase_  :	Union[str, Any]				      =		shift_tokens_right(lowercase_     ,1     ,2)

             UpperCAmelCase_  :	Tuple				      =		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=lowercase_     ,)
             UpperCAmelCase_  :	Dict				      =		prepare_blenderbot_inputs_dict(lowercase_     ,lowercase_     ,lowercase_)
             return config, inputs_dict



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[Any]				      =		self.prepare_config_and_inputs()
             return config, inputs_dict



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		20
             UpperCAmelCase_  :	int				      =		model_class_name(lowercase_)

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

             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

             UpperCAmelCase_  :	Optional[Any]				      =		model.init_cache(decoder_input_ids.shape[0]     ,lowercase_     ,lowercase_)
             UpperCAmelCase_  :	Optional[Any]				      =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype="i4")

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

             UpperCAmelCase_  :	Union[str, Any]				      =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype="i4")
             UpperCAmelCase_  :	List[str]				      =		model.decode(
                 decoder_input_ids[:, -1:]     ,lowercase_     ,decoder_attention_mask=lowercase_     ,past_key_values=outputs_cache.past_key_values     ,decoder_position_ids=lowercase_     ,)

             UpperCAmelCase_  :	Tuple				      =		model.decode(lowercase_     ,lowercase_)

             UpperCAmelCase_  :	Optional[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  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		20
             UpperCAmelCase_  :	List[Any]				      =		model_class_name(lowercase_)

             UpperCAmelCase_  :	str				      =		model.encode(inputs_dict["input_ids"])

             UpperCAmelCase_      ,      UpperCAmelCase_  :	Dict				      =		(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

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

             UpperCAmelCase_  :	str				      =		model.init_cache(decoder_input_ids.shape[0]     ,lowercase_     ,lowercase_)
             UpperCAmelCase_  :	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)     ,)

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

             UpperCAmelCase_  :	Any				      =		model.decode(lowercase_     ,lowercase_     ,decoder_attention_mask=lowercase_)

             UpperCAmelCase_  :	Union[str, 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}""")


@require_flax
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				99



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		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     ,)

             UpperCAmelCase_  :	List[Any]				      =		input_ids.shape[0]
             UpperCAmelCase_  :	Any				      =		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):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Any				      =		self._get_config_and_data()
             UpperCAmelCase_  :	Optional[Any]				      =		FlaxBlenderbotSmallForConditionalGeneration(lowercase_)
             UpperCAmelCase_  :	List[str]				      =		lm_model(input_ids=lowercase_)
             UpperCAmelCase_  :	Optional[Any]				      =		(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape     ,lowercase_)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		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     ,)
             UpperCAmelCase_  :	List[str]				      =		FlaxBlenderbotSmallForConditionalGeneration(lowercase_)
             UpperCAmelCase_  :	List[Any]				      =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,dtype=np.intaa)
             UpperCAmelCase_  :	str				      =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,dtype=np.intaa)
             UpperCAmelCase_  :	str				      =		lm_model(input_ids=lowercase_     ,decoder_input_ids=lowercase_)
             UpperCAmelCase_  :	int				      =		(*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape     ,lowercase_)



       def  A_   (     self):

             """simple docstring"""





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


@require_flax
class   snake_case_ (lowercase__ , unittest.TestCase , lowercase__  ):




       """simple docstring"""






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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		FlaxBlenderbotSmallModelTester(self)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(lowercase_     ,lowercase_     ,lowercase_)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		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(lowercase_     ,lowercase_     ,lowercase_)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	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__):
                         UpperCAmelCase_  :	List[Any]				      =		self._prepare_for_class(lowercase_     ,lowercase_)
                         UpperCAmelCase_  :	int				      =		model_class(lowercase_)

                         @jax.jit
                         def encode_jitted(lowercase     ,lowercase=None     ,**lowercase):
                               return model.encode(input_ids=lowercase_     ,attention_mask=lowercase_)

                         with self.subTest("JIT Enabled"):
                               UpperCAmelCase_  :	Optional[int]				      =		encode_jitted(**lowercase_).to_tuple()

                         with self.subTest("JIT Disabled"):
                               with jax.disable_jit():
                                     UpperCAmelCase_  :	List[Any]				      =		encode_jitted(**lowercase_).to_tuple()

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__):
                         UpperCAmelCase_  :	Tuple				      =		model_class(lowercase_)
                         UpperCAmelCase_  :	Dict				      =		model.encode(inputs_dict["input_ids"]     ,inputs_dict["attention_mask"])

                         UpperCAmelCase_  :	List[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(lowercase     ,lowercase     ,lowercase):
                               return model.decode(
                                   decoder_input_ids=lowercase_     ,decoder_attention_mask=lowercase_     ,encoder_outputs=lowercase_     ,)

                         with self.subTest("JIT Enabled"):
                               UpperCAmelCase_  :	Dict				      =		decode_jitted(**lowercase_).to_tuple()

                         with self.subTest("JIT Disabled"):
                               with jax.disable_jit():
                                     UpperCAmelCase_  :	List[str]				      =		decode_jitted(**lowercase_).to_tuple()

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



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_  :	Union[str, Any]				      =		model_class_name.from_pretrained("facebook/blenderbot_small-90M")
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   UpperCAmelCase_  :	Tuple				      =		np.ones((1, 1)) * model.config.eos_token_id
                   UpperCAmelCase_  :	str				      =		model(lowercase_)
                   self.assertIsNotNone(lowercase_)





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch


class   snake_case_ (TensorFormatter[Mapping, """torch.Tensor""", Mapping]  ):




       """simple docstring"""






       def __init__(     self     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             super().__init__(features=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		torch_tensor_kwargs
             import torch  # noqa import torch at initialization



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             if isinstance(lowercase     ,lowercase) and column:
                   if all(
                       isinstance(lowercase     ,torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
                       for x in column):
                         return torch.stack(lowercase)
             return column



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             if isinstance(lowercase     ,(str, bytes, type(lowercase))):
                   return value
             elif isinstance(lowercase     ,(np.character, np.ndarray)) and np.issubdtype(value.dtype     ,np.character):
                   return value.tolist()

             UpperCAmelCase_  :	List[str]				      =		{}

             if isinstance(lowercase     ,(np.number, np.ndarray)) and np.issubdtype(value.dtype     ,np.integer):
                   UpperCAmelCase_  :	Dict				      =		{"dtype": torch.intaa}
             elif isinstance(lowercase     ,(np.number, np.ndarray)) and np.issubdtype(value.dtype     ,np.floating):
                   UpperCAmelCase_  :	Optional[Any]				      =		{"dtype": torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                   import PIL.Image

                   if isinstance(lowercase     ,PIL.Image.Image):
                         UpperCAmelCase_  :	List[str]				      =		np.asarray(lowercase)
             return torch.tensor(lowercase     ,**{**default_dtype, **self.torch_tensor_kwargs})



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             # support for torch, tf, jax etc.
             if hasattr(lowercase     ,"__array__") and not isinstance(lowercase     ,torch.Tensor):
                   UpperCAmelCase_  :	int				      =		data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(lowercase     ,np.ndarray):
                   if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
             elif isinstance(lowercase     ,(list, tuple)):
                   return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
             return self._tensorize(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return map_nested(self._recursive_tensorize     ,lowercase     ,map_list=lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.numpy_arrow_extractor().extract_row(lowercase)
             UpperCAmelCase_  :	Tuple				      =		self.python_features_decoder.decode_row(lowercase)
             return self.recursive_tensorize(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.numpy_arrow_extractor().extract_column(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.python_features_decoder.decode_column(lowercase     ,pa_table.column_names[0])
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             UpperCAmelCase_  :	List[str]				      =		self._consolidate(lowercase)
             return column



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.numpy_arrow_extractor().extract_batch(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		self.python_features_decoder.decode_batch(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             for column_name in batch:
                   UpperCAmelCase_  :	Optional[int]				      =		self._consolidate(batch[column_name])
             return batch
