



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
 __snake_case						:Union[str, 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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			):
    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    _A	:



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Any=13					,		__SCREAMING_SNAKE_CASE							:					List[Any]=7					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					Tuple=False					,		__SCREAMING_SNAKE_CASE							:					str=99					,		__SCREAMING_SNAKE_CASE							:					Any=16					,		__SCREAMING_SNAKE_CASE							:					str=2					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=4					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=4					,		__SCREAMING_SNAKE_CASE							:					str="gelu"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.1					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Any=32					,		__SCREAMING_SNAKE_CASE							:					Dict=2					,		__SCREAMING_SNAKE_CASE							:					Any=1					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=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[int]):


           '''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(__SCREAMING_SNAKE_CASE					,		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=__SCREAMING_SNAKE_CASE					,		)
           __a							=    prepare_blenderbot_inputs_dict(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return config, inputs_dict



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    20
           __a							=    model_class_name(__SCREAMING_SNAKE_CASE)

           __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]					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __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]					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		past_key_values=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)

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

           __a							=    model.decode(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           __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							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           __a							=    20
           __a							=    model_class_name(__SCREAMING_SNAKE_CASE)

           __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]					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __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]					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		past_key_values=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)
           __a							=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,		dtype='''i4''')
           __a							=    model.decode(
               decoder_input_ids[:, -1:]					,		__SCREAMING_SNAKE_CASE					,		past_key_values=outputs_cache.past_key_values					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)

           __a							=    model.decode(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE)

           __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    _A	(     unittest.TestCase   ):
       UpperCamelCase__  :   Dict						 =						99



       def 		_lowerCamelCase      (		self							:					int):


           '''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							:					Optional[Any]):


           '''simple docstring'''

           __a	,       __a	,       __a							=    self._get_config_and_data()
           __a							=    FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE)
           __a							=    lm_model(input_ids=__SCREAMING_SNAKE_CASE)
           __a							=    (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					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(__SCREAMING_SNAKE_CASE)
           __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=__SCREAMING_SNAKE_CASE					,		decoder_input_ids=__SCREAMING_SNAKE_CASE)
           __a							=    (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					List[str]):


           '''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(__SCREAMING_SNAKE_CASE					,		1					,		2)
           __a							=    np.equal(__SCREAMING_SNAKE_CASE					,		1).astype(np.floataa).sum()
           __a							=    np.equal(__SCREAMING_SNAKE_CASE					,		1).astype(np.floataa).sum()
           self.assertEqual(shifted.shape					,		input_ids.shape)
           self.assertEqual(__SCREAMING_SNAKE_CASE					,		n_pad_before - 1)
           self.assertTrue(np.equal(shifted[:, 0]					,		2).all())


@require_flax
class    _A	(     __UpperCAmelCase				,unittest.TestCase				,__UpperCAmelCase   ):
       UpperCamelCase__  :   Optional[Any]						 =						True
       UpperCamelCase__  :   Optional[int]						 =						(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase__  :   List[Any]						 =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    FlaxBlenderbotModelTester(self)



       def 		_lowerCamelCase      (		self							:					str):


           '''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(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''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(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''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(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_class(__SCREAMING_SNAKE_CASE)

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

                   with self.subTest('''JIT Enabled'''):
                       __a							=    encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a							=    encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

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



       def 		_lowerCamelCase      (		self							:					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(__SCREAMING_SNAKE_CASE)
                   __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(__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any):
                       return model.decode(
                           decoder_input_ids=__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		encoder_outputs=__SCREAMING_SNAKE_CASE					,		)

                   with self.subTest('''JIT Enabled'''):
                       __a							=    decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a							=    decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

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



       @slow
       def 		_lowerCamelCase      (		self							:					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(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @unittest.skipUnless(jax_device != '''cpu'''					,		'''3B test too slow on CPU.''')
       @slow
       def 		_lowerCamelCase      (		self							:					Dict):


           '''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=__SCREAMING_SNAKE_CASE)
           __a							=    BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''')

           __a							=    ['''Sam''']
           __a							=    tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors='''jax''')

           __a							=    model.generate(**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    '''Sam is a great name. It means "sun" in Gaelic.'''

           __a							=    tokenizer.batch_decode(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           assert generated_txt[0].strip() == tgt_text










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


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[Any]						=					{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''gptsan-japanese'''
       UpperCamelCase__  :   Dict						 =						[
           '''past_key_values''',
       ]
       UpperCamelCase__  :   Dict						 =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=36_000					,		__SCREAMING_SNAKE_CASE							:					Tuple=1_280					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=8_192					,		__SCREAMING_SNAKE_CASE							:					str=4_096					,		__SCREAMING_SNAKE_CASE							:					Any=128					,		__SCREAMING_SNAKE_CASE							:					int=10					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=128					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1E-5					,		__SCREAMING_SNAKE_CASE							:					str=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]="float32"					,		__SCREAMING_SNAKE_CASE							:					List[str]=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					int=0.0_02					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=35_998					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=35_995					,		__SCREAMING_SNAKE_CASE							:					List[str]=35_999					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    max_position_embeddings
           __a							=    d_model
           __a							=    d_ff
           __a							=    d_ext
           __a							=    d_spout
           __a							=    num_switch_layers
           __a							=    num_ext_layers
           __a							=    num_switch_layers + num_ext_layers
           __a							=    num_heads
           __a							=    num_experts
           __a							=    expert_capacity
           __a							=    dropout_rate
           __a							=    layer_norm_epsilon
           __a							=    router_bias
           __a							=    router_jitter_noise
           __a							=    router_dtype
           __a							=    router_ignore_padding_tokens
           __a							=    output_hidden_states
           __a							=    output_attentions
           __a							=    initializer_factor
           __a							=    output_router_logits
           __a							=    use_cache

           super().__init__(
               separator_token_id=__SCREAMING_SNAKE_CASE					,		pad_token_id=__SCREAMING_SNAKE_CASE					,		eos_token_id=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





