"""simple docstring"""
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
  SCREAMING_SNAKE_CASE__   :      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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_=None						,	SCREAMING_SNAKE_CASE_=None						,	SCREAMING_SNAKE_CASE_=None						,	SCREAMING_SNAKE_CASE_=None						,	SCREAMING_SNAKE_CASE_=None						,	SCREAMING_SNAKE_CASE_=None						,	)      ->Optional[Any]:
    if attention_mask is None:
        _lowerCamelCase	: Optional[Any]       =       np.where(input_ids != config.pad_token_id						,	1						,	0 )
    if decoder_attention_mask is None:
        _lowerCamelCase	: str       =       np.where(decoder_input_ids != config.pad_token_id						,	1						,	0 )
    if head_mask is None:
        _lowerCamelCase	: str       =       np.ones((config.encoder_layers, config.encoder_attention_heads) )
    if decoder_head_mask is None:
        _lowerCamelCase	: Dict       =       np.ones((config.decoder_layers, config.decoder_attention_heads) )
    if cross_attn_head_mask is None:
        _lowerCamelCase	: Tuple       =       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      _UpperCAmelCase						:






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







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

        _lowerCamelCase	: Optional[Any]       =       shift_tokens_right(_lowercase    , 1    , 2					)

        _lowerCamelCase	: List[Any]       =       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=_lowercase    , )
        _lowerCamelCase	: Union[str, Any]       =       prepare_blenderbot_inputs_dict(_lowercase    , _lowercase    , _lowercase					)
        return config, inputs_dict







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase,					_lowerCamelCase	: Optional[Any]       =       self.prepare_config_and_inputs()
        return config, inputs_dict







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase					)	->	Any:
        _lowerCamelCase	: Any       =       20
        _lowerCamelCase	: Tuple       =       model_class_name(_lowercase					)

        _lowerCamelCase	: Any       =       model.encode(inputs_dict['''input_ids''']					)

        _lowerCamelCase,					_lowerCamelCase	: int       =       (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        _lowerCamelCase	: Optional[int]       =       model.init_cache(decoder_input_ids.shape[0]    , _lowercase    , _lowercase					)
        _lowerCamelCase	: int       =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    , dtype='''i4'''					)

        _lowerCamelCase	: List[Any]       =       jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]    , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    , )
        _lowerCamelCase	: Any       =       model.decode(
            decoder_input_ids[:, :-1]    , _lowercase    , decoder_attention_mask=_lowercase    , past_key_values=_lowercase    , decoder_position_ids=_lowercase    , )

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

        _lowerCamelCase	: List[Any]       =       model.decode(_lowercase    , _lowercase					)

        _lowerCamelCase	: 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 				a__					(			self    , _lowercase    , _lowercase    , _lowercase					)	->	Any:
        _lowerCamelCase	: List[Any]       =       20
        _lowerCamelCase	: Dict       =       model_class_name(_lowercase					)

        _lowerCamelCase	: int       =       model.encode(inputs_dict['''input_ids''']					)

        _lowerCamelCase,					_lowerCamelCase	: List[Any]       =       (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        _lowerCamelCase	: Union[str, Any]       =       jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
            ]    , axis=-1    , )

        _lowerCamelCase	: Optional[int]       =       model.init_cache(decoder_input_ids.shape[0]    , _lowercase    , _lowercase					)
        _lowerCamelCase	: Tuple       =       jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]    , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    , )

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

        _lowerCamelCase	: Optional[int]       =       model.decode(_lowercase    , _lowercase    , decoder_attention_mask=_lowercase					)

        _lowerCamelCase	: 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}'''					)





@require_flax
class      _UpperCAmelCase						( unittest.TestCase						):






    """simple docstring"""
    __snake_case    =       99







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase	: Union[str, Any]       =       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    , )

        _lowerCamelCase	: List[Any]       =       input_ids.shape[0]
        _lowerCamelCase	: Dict       =       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					)	->	Dict:
        _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	: str       =       self._get_config_and_data()
        _lowerCamelCase	: Optional[int]       =       FlaxBlenderbotForConditionalGeneration(_lowercase					)
        _lowerCamelCase	: Tuple       =       lm_model(input_ids=_lowercase					)
        _lowerCamelCase	: Optional[Any]       =       (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape    , _lowercase					)







    def 				a__					(			self					)	->	Dict:
        _lowerCamelCase	: Tuple       =       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    , )
        _lowerCamelCase	: Dict       =       FlaxBlenderbotForConditionalGeneration(_lowercase					)
        _lowerCamelCase	: Union[str, Any]       =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    , dtype=np.intaa					)
        _lowerCamelCase	: str       =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    , dtype=np.intaa					)
        _lowerCamelCase	: str       =       lm_model(input_ids=_lowercase    , decoder_input_ids=_lowercase					)
        _lowerCamelCase	: int       =       (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape    , _lowercase					)







    def 				a__					(			self					)	->	str:
        _lowerCamelCase	: List[Any]       =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    , dtype=np.intaa					)
        _lowerCamelCase	: List[Any]       =       shift_tokens_right(_lowercase    , 1    , 2					)
        _lowerCamelCase	: Dict       =       np.equal(_lowercase    , 1					).astype(np.floataa					).sum()
        _lowerCamelCase	: str       =       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      _UpperCAmelCase						( A__ ,			unittest.TestCase ,			A__						):






    """simple docstring"""
    __snake_case    =       True
    __snake_case    =       (
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    __snake_case    =       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







    def 				a__					(			self					)	->	Optional[int]:
        _lowerCamelCase	: List[str]       =       FlaxBlenderbotModelTester(self					)







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase,					_lowerCamelCase	: List[Any]       =       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					)	->	Optional[int]:
        _lowerCamelCase,					_lowerCamelCase	: List[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					)	->	str:
        _lowerCamelCase,					_lowerCamelCase	: Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
                _lowerCamelCase	: int       =       self._prepare_for_class(_lowercase    , _lowercase					)
                _lowerCamelCase	: Dict       =       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'''					):
                    _lowerCamelCase	: Dict       =       encode_jitted(**_lowercase					).to_tuple()

                with self.subTest('''JIT Disabled'''					):
                    with jax.disable_jit():
                        _lowerCamelCase	: int       =       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					)	->	str:
        _lowerCamelCase,					_lowerCamelCase	: List[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
                _lowerCamelCase	: Optional[int]       =       model_class(_lowercase					)
                _lowerCamelCase	: Dict       =       model.encode(inputs_dict['''input_ids''']    , inputs_dict['''attention_mask''']					)

                _lowerCamelCase	: List[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(_lowercase    , _lowercase    , _lowercase					):
                    return model.decode(
                        decoder_input_ids=_lowercase    , decoder_attention_mask=_lowercase    , encoder_outputs=_lowercase    , )

                with self.subTest('''JIT Enabled'''					):
                    _lowerCamelCase	: Union[str, Any]       =       decode_jitted(**_lowercase					).to_tuple()

                with self.subTest('''JIT Disabled'''					):
                    with jax.disable_jit():
                        _lowerCamelCase	: int       =       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					)	->	List[str]:
        for model_class_name in self.all_model_classes:
            _lowerCamelCase	: Tuple       =       model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''					)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            _lowerCamelCase	: Any       =       np.ones((1, 1)					) * model.config.eos_token_id
            _lowerCamelCase	: Union[str, Any]       =       model(_lowercase					)
            self.assertIsNotNone(_lowercase					)







    @unittest.skipUnless(jax_device != '''cpu'''    , '''3B test too slow on CPU.'''					)
    @slow
    def 				a__					(			self					)	->	Any:
        _lowerCamelCase	: Dict       =       {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
        _lowerCamelCase	: Tuple       =       {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

        _lowerCamelCase	: List[Any]       =       FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    , from_pt=_lowercase					)
        _lowerCamelCase	: Union[str, Any]       =       BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''					)

        _lowerCamelCase	: Any       =       ['''Sam''']
        _lowerCamelCase	: Dict       =       tokenizer(_lowercase    , return_tensors='''jax'''					)

        _lowerCamelCase	: str       =       model.generate(**_lowercase    , **_lowercase					)
        _lowerCamelCase	: Optional[Any]       =       '''Sam is a great name. It means "sun" in Gaelic.'''

        _lowerCamelCase	: Dict       =       tokenizer.batch_decode(_lowercase    , **_lowercase					)
        assert generated_txt[0].strip() == tgt_text










def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




