

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
    UpperCamelCase__		 =     '''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 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :Union[str, Any]	,__A    :List[str]=None	,__A    :Union[str, Any]=None	,__A    :Union[str, Any]=None	,__A    :str=None	,__A    :List[Any]=None	,__A    :Any=None	,):


       """simple docstring"""




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






              """simple docstring"""


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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


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

              __snake_case       	=							shift_tokens_right(_UpperCamelCase      , 1      , 2   )

              __snake_case       	=							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      , )
              __snake_case       	=							prepare_blenderbot_inputs_dict(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
              return config, inputs_dict


       def 			a		(		self   )      ->							int:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							20
              __snake_case       	=							model_class_name(_UpperCamelCase   )

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

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

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

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

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

              __snake_case       	=							model.decode(_UpperCamelCase      , _UpperCamelCase   )

              __snake_case       	=							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      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							20
              __snake_case       	=							model_class_name(_UpperCamelCase   )

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

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

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

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

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

              __snake_case       	=							model.decode(_UpperCamelCase      , _UpperCamelCase      , decoder_attention_mask=_UpperCamelCase   )

              __snake_case       	=							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"""







       __SCREAMING_SNAKE_CASE				    =							99


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							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      , )

              __snake_case       	=							input_ids.shape[0]
              __snake_case       	=							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 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


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






       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      , dtype=np.intaa   )
              __snake_case       	=							shift_tokens_right(_UpperCamelCase      , 1      , 2   )
              __snake_case       	=							np.equal(_UpperCamelCase      , 1   ).astype(np.floataa   ).sum()
              __snake_case       	=							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     __snake_case    (				snake_case__			,	unittest.TestCase			,	snake_case__  ):
       """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 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							FlaxBlenderbotModelTester(self   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							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 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							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 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__   ):
                            __snake_case       	=							self._prepare_for_class(_UpperCamelCase      , _UpperCamelCase   )
                            __snake_case       	=							model_class(_UpperCamelCase   )

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

                            with self.subTest("""JIT Enabled"""   ):
                                   __snake_case       	=							encode_jitted(**_UpperCamelCase   ).to_tuple()

                            with self.subTest("""JIT Disabled"""   ):
                                   with jax.disable_jit():
                                          __snake_case       	=							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 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

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

                            __snake_case       	=							{
                                """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=_UpperCamelCase      , decoder_attention_mask=_UpperCamelCase      , encoder_outputs=_UpperCamelCase      , )

                            with self.subTest("""JIT Enabled"""   ):
                                   __snake_case       	=							decode_jitted(**_UpperCamelCase   ).to_tuple()

                            with self.subTest("""JIT Disabled"""   ):
                                   with jax.disable_jit():
                                          __snake_case       	=							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 			a		(		self   )      ->							int:






              """simple docstring"""


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






       @unittest.skipUnless(jax_device != """cpu"""      , """3B test too slow on CPU."""   )
       @slow
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


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

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

              __snake_case       	=							["""Sam"""]
              __snake_case       	=							tokenizer(_UpperCamelCase      , return_tensors="""jax"""   )

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

              __snake_case       	=							tokenizer.batch_decode(_UpperCamelCase      , **_UpperCamelCase   )
              assert generated_txt[0].strip() == tgt_text




from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

