






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
  __a   :							List[Any]						= "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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[int]      ,					__lowercase :      str      ,					__lowercase :      Union[str, Any]=None      ,					__lowercase :      str=None      ,					__lowercase :      Optional[Any]=None      ,					__lowercase :      Optional[int]=None      ,					__lowercase :      Union[str, Any]=None      ,					__lowercase :      Dict=None      ,					)    ->   List[str]:





    """simple docstring"""

    if attention_mask is None:
        __A       =						np.where(input_ids != config.pad_token_id      ,					1      ,					0     )
    if decoder_attention_mask is None:
        __A       =						np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0     )
    if head_mask is None:
        __A       =						np.ones((config.encoder_layers, config.encoder_attention_heads)     )
    if decoder_head_mask is None:
        __A       =						np.ones((config.decoder_layers, config.decoder_attention_heads)     )
    if cross_attn_head_mask is None:
        __A       =						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							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict=13			,						UpperCamelCase_							:      Any=7			,						UpperCamelCase_							:      Optional[Any]=True			,						UpperCamelCase_							:      Union[str, Any]=False			,						UpperCamelCase_							:      str=99			,						UpperCamelCase_							:      int=16			,						UpperCamelCase_							:      List[str]=2			,						UpperCamelCase_							:      Union[str, Any]=4			,						UpperCamelCase_							:      Dict=4			,						UpperCamelCase_							:      Optional[Any]="gelu"			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      Optional[Any]=2			,						UpperCamelCase_							:      int=1			,						UpperCamelCase_							:      int=0			,						UpperCamelCase_							:      Optional[int]=0.02			,						):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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

         __A       =						shift_tokens_right(UpperCamelCase_			,						1			,						2					)

         __A       =						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=UpperCamelCase_			,						)
         __A       =						prepare_blenderbot_inputs_dict(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
         return config, inputs_dict




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


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[Any]					):


         """simple docstring"""



         __A       =						20
         __A       =						model_class_name(UpperCamelCase_					)

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

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

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

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

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

         __A       =						model.decode(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						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							:      Any			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						20
         __A       =						model_class_name(UpperCamelCase_					)

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

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

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

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

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

         __A       =						model.decode(UpperCamelCase_			,						UpperCamelCase_			,						decoder_attention_mask=UpperCamelCase_					)

         __A       =						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'''
     SCREAMING_SNAKE_CASE     =					99




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						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			,						)

         __A       =						input_ids.shape[0]
         __A       =						BlenderbotConfig(
             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							:      Union[str, Any]					):


         """simple docstring"""



         __A     ,						__A     ,						__A       =						self._get_config_and_data()
         __A       =						FlaxBlenderbotForConditionalGeneration(UpperCamelCase_					)
         __A       =						lm_model(input_ids=UpperCamelCase_					)
         __A       =						(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



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


@require_flax
class    __lowercase   (						lowercase_      ,   unittest.TestCase      ,   lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     SCREAMING_SNAKE_CASE     =					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						FlaxBlenderbotModelTester(self					)




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


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)




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


         """simple docstring"""



         __A     ,						__A       =						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(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__					):
                 __A       =						self._prepare_for_class(UpperCamelCase_			,						UpperCamelCase_					)
                 __A       =						model_class(UpperCamelCase_					)

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

                 with self.subTest("""JIT Enabled"""					):
                     __A       =						encode_jitted(**UpperCamelCase_					).to_tuple()

                 with self.subTest("""JIT Disabled"""					):
                     with jax.disable_jit():
                         __A       =						encode_jitted(**UpperCamelCase_					).to_tuple()

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




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

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

                 __A       =						{
                     """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_							:      List[str]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      int					):
                     return model.decode(
                         decoder_input_ids=UpperCamelCase_			,						decoder_attention_mask=UpperCamelCase_			,						encoder_outputs=UpperCamelCase_			,						)

                 with self.subTest("""JIT Enabled"""					):
                     __A       =						decode_jitted(**UpperCamelCase_					).to_tuple()

                 with self.subTest("""JIT Disabled"""					):
                     with jax.disable_jit():
                         __A       =						decode_jitted(**UpperCamelCase_					).to_tuple()

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




     @slow
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



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





     @unittest.skipUnless(jax_device != """cpu"""			,						"""3B test too slow on CPU."""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
         __A       =						{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

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

         __A       =						["""Sam"""]
         __A       =						tokenizer(UpperCamelCase_			,						return_tensors="""jax"""					)

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

         __A       =						tokenizer.batch_decode(UpperCamelCase_			,						**UpperCamelCase_					)
         assert generated_txt[0].strip() == tgt_text












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a   :							Optional[Any]						= logging.get_logger(__name__)

__a   :							Dict						= {
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"realm"




     def __init__(					self							:      str			,						UpperCamelCase_							:      List[Any]=30_522			,						UpperCamelCase_							:      Dict=768			,						UpperCamelCase_							:      Union[str, Any]=128			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      Optional[Any]=12			,						UpperCamelCase_							:      Optional[int]=8			,						UpperCamelCase_							:      str=3_072			,						UpperCamelCase_							:      List[str]="gelu_new"			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      str=512			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      str=0.02			,						UpperCamelCase_							:      List[Any]=1e-12			,						UpperCamelCase_							:      str=256			,						UpperCamelCase_							:      List[str]=10			,						UpperCamelCase_							:      int=1e-3			,						UpperCamelCase_							:      List[str]=5			,						UpperCamelCase_							:      Tuple=320			,						UpperCamelCase_							:      List[str]=13_353_718			,						UpperCamelCase_							:      Tuple=5_000			,						UpperCamelCase_							:      Optional[int]=1			,						UpperCamelCase_							:      List[str]=0			,						UpperCamelCase_							:      Union[str, Any]=2			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         # Common config
         __A       =						vocab_size
         __A       =						max_position_embeddings
         __A       =						hidden_size
         __A       =						retriever_proj_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						num_candidates
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						initializer_range
         __A       =						type_vocab_size
         __A       =						layer_norm_eps

         # Reader config
         __A       =						span_hidden_size
         __A       =						max_span_width
         __A       =						reader_layer_norm_eps
         __A       =						reader_beam_size
         __A       =						reader_seq_len

         # Retrieval config
         __A       =						num_block_records
         __A       =						searcher_beam_size




