
"""simple docstring"""




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
    A__      :	Dict          =    """platform"""

    import jax
    import jax.numpy as jnp

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



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Optional[Any]=None       ,    lowerCAmelCase	:       Dict=None       ,    lowerCAmelCase	:       Any=None       ,    lowerCAmelCase	:       Dict=None       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       Union[str, Any]=None       ,    ):






     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__							: str 							=						np.where(input_ids != config.pad_token_id       ,    1       ,    0		)
     if decoder_attention_mask is None:
          UpperCAmelCase__							: Union[str, Any] 							=						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__							: int 							=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          UpperCAmelCase__							: List[Any] 							=						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   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=13 ,			__UpperCamelCase=7 ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=99 ,			__UpperCamelCase=16 ,			__UpperCamelCase=2 ,			__UpperCamelCase=4 ,			__UpperCamelCase=4 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=32 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=0 ,			__UpperCamelCase=0.02 ,			)->					Optional[Any]:
          UpperCAmelCase__							: List[str] 							=						parent
          UpperCAmelCase__							: Optional[int] 							=						batch_size
          UpperCAmelCase__							: List[str] 							=						seq_length
          UpperCAmelCase__							: Optional[Any] 							=						is_training
          UpperCAmelCase__							: List[str] 							=						use_labels
          UpperCAmelCase__							: Tuple 							=						vocab_size
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_size
          UpperCAmelCase__							: Optional[Any] 							=						num_hidden_layers
          UpperCAmelCase__							: Optional[Any] 							=						num_attention_heads
          UpperCAmelCase__							: List[Any] 							=						intermediate_size
          UpperCAmelCase__							: Tuple 							=						hidden_act
          UpperCAmelCase__							: Optional[int] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: List[str] 							=						max_position_embeddings
          UpperCAmelCase__							: str 							=						eos_token_id
          UpperCAmelCase__							: Any 							=						pad_token_id
          UpperCAmelCase__							: Any 							=						bos_token_id
          UpperCAmelCase__							: Optional[int] 							=						initializer_range



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Optional[int] 							=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,			self.vocab_size  ) ,			3 ,			self.vocab_size  )
          UpperCAmelCase__							: int 							=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,			dtype=np.intaa  )) ,			-1  )

          UpperCAmelCase__							: Optional[int] 							=						shift_tokens_right(lowerCamelCase_ ,			1 ,			2  )

          UpperCAmelCase__							: Any 							=						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=lowerCamelCase_ ,			)
          UpperCAmelCase__							: Optional[int] 							=						prepare_blenderbot_inputs_dict(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_  )
          return config, inputs_dict



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.prepare_config_and_inputs()
          return config, inputs_dict



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: Optional[int] 							=						20
          UpperCAmelCase__							: Optional[Any] 							=						model_class_name(lowerCamelCase_  )

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

          UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          UpperCAmelCase__							: Tuple 							=						model.init_cache(decoder_input_ids.shape[0] ,			lowerCamelCase_ ,			lowerCamelCase_  )
          UpperCAmelCase__							: Tuple 							=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,			dtype="i4"  )

          UpperCAmelCase__							: 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__							: int 							=						model.decode(
              decoder_input_ids[:, :-1] ,			lowerCamelCase_ ,			decoder_attention_mask=lowerCamelCase_ ,			past_key_values=lowerCamelCase_ ,			decoder_position_ids=lowerCamelCase_ ,			)

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

          UpperCAmelCase__							: Union[str, Any] 							=						model.decode(lowerCamelCase_ ,			lowerCamelCase_  )

          UpperCAmelCase__							: 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__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: str 							=						20
          UpperCAmelCase__							: List[str] 							=						model_class_name(lowerCamelCase_  )

          UpperCAmelCase__							: Union[str, Any] 							=						model.encode(inputs_dict["input_ids"]  )

          UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          UpperCAmelCase__							: Optional[Any] 							=						jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
              ] ,			axis=-1 ,			)

          UpperCAmelCase__							: Optional[int] 							=						model.init_cache(decoder_input_ids.shape[0] ,			lowerCamelCase_ ,			lowerCamelCase_  )
          UpperCAmelCase__							: 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__							: int 							=						model.decode(
              decoder_input_ids[:, :-1] ,			lowerCamelCase_ ,			decoder_attention_mask=lowerCamelCase_ ,			past_key_values=lowerCamelCase_ ,			decoder_position_ids=lowerCamelCase_ ,			)
          UpperCAmelCase__							: List[Any] 							=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,			dtype="i4"  )
          UpperCAmelCase__							: Optional[int] 							=						model.decode(
              decoder_input_ids[:, -1:] ,			lowerCamelCase_ ,			past_key_values=outputs_cache.past_key_values ,			decoder_attention_mask=lowerCamelCase_ ,			decoder_position_ids=lowerCamelCase_ ,			)

          UpperCAmelCase__							: str 							=						model.decode(lowerCamelCase_ ,			lowerCamelCase_ ,			decoder_attention_mask=lowerCamelCase_  )

          UpperCAmelCase__							: str 							=						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   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     _A								=							99



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Optional[int] 							=						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__							: Dict 							=						input_ids.shape[0]
          UpperCAmelCase__							: int 							=						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  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						self._get_config_and_data()
          UpperCAmelCase__							: Optional[int] 							=						FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_  )
          UpperCAmelCase__							: str 							=						lm_model(input_ids=lowerCamelCase_  )
          UpperCAmelCase__							: Union[str, Any] 							=						(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["logits"].shape ,			lowerCamelCase_  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: List[Any] 							=						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(lowerCamelCase_  )
          UpperCAmelCase__							: int 							=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,			dtype=np.intaa  )
          UpperCAmelCase__							: Any 							=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,			dtype=np.intaa  )
          UpperCAmelCase__							: Union[str, Any] 							=						lm_model(input_ids=lowerCamelCase_ ,			decoder_input_ids=lowerCamelCase_  )
          UpperCAmelCase__							: int 							=						(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape ,			lowerCamelCase_  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Any 							=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,			dtype=np.intaa  )
          UpperCAmelCase__							: str 							=						shift_tokens_right(lowerCamelCase_ ,			1 ,			2  )
          UpperCAmelCase__							: Dict 							=						np.equal(lowerCamelCase_ ,			1  ).astype(np.floataa  ).sum()
          UpperCAmelCase__							: Tuple 							=						np.equal(lowerCamelCase_ ,			1  ).astype(np.floataa  ).sum()
          self.assertEqual(shifted.shape ,			input_ids.shape  )
          self.assertEqual(lowerCamelCase_ ,			n_pad_before - 1  )
          self.assertTrue(np.equal(shifted[:, 0] ,			2  ).all()  )







@require_flax
class   _lowercase						(       a__	,   unittest.TestCase	,   a__				):


     '''simple docstring'''





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



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: int 							=						FlaxBlenderbotSmallModelTester(self  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          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(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          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_with_attn_mask(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							,	UpperCAmelCase__							: 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__  ):
                    UpperCAmelCase__							: Tuple 							=						self._prepare_for_class(lowerCamelCase_ ,			lowerCamelCase_  )
                    UpperCAmelCase__							: str 							=						model_class(lowerCamelCase_  )

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

                    with self.subTest("JIT Enabled"  ):
                         UpperCAmelCase__							: Tuple 							=						encode_jitted(**lowerCamelCase_  ).to_tuple()

                    with self.subTest("JIT Disabled"  ):
                         with jax.disable_jit():
                              UpperCAmelCase__							: List[str] 							=						encode_jitted(**lowerCamelCase_  ).to_tuple()

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



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          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__							: Union[str, Any] 							=						model_class(lowerCamelCase_  )
                    UpperCAmelCase__							: int 							=						model.encode(inputs_dict["input_ids"] ,			inputs_dict["attention_mask"]  )

                    UpperCAmelCase__							: str 							=						{
                        "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(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  ):
                         return model.decode(
                             decoder_input_ids=lowerCamelCase_ ,			decoder_attention_mask=lowerCamelCase_ ,			encoder_outputs=lowerCamelCase_ ,			)

                    with self.subTest("JIT Enabled"  ):
                         UpperCAmelCase__							: Optional[Any] 							=						decode_jitted(**lowerCamelCase_  ).to_tuple()

                    with self.subTest("JIT Disabled"  ):
                         with jax.disable_jit():
                              UpperCAmelCase__							: Optional[int] 							=						decode_jitted(**lowerCamelCase_  ).to_tuple()

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



     @slow
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          for model_class_name in self.all_model_classes:
               UpperCAmelCase__							: Optional[int] 							=						model_class_name.from_pretrained("facebook/blenderbot_small-90M"  )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               UpperCAmelCase__							: Any 							=						np.ones((1, 1)  ) * model.config.eos_token_id
               UpperCAmelCase__							: List[Any] 							=						model(lowerCamelCase_  )
               self.assertIsNotNone(lowerCamelCase_  )





"""simple docstring"""




from typing import TYPE_CHECKING

from ....utils import _LazyModule


A__      :	List[str]          =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    A__      :	Any          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



