





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[str]									=		"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__(       UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       str=None ,     UpperCamelCase__			:       Union[str, Any]=None ,     UpperCamelCase__			:       List[Any]=None ,     UpperCamelCase__			:       Any=None ,     UpperCamelCase__			:       Any=None ,     UpperCamelCase__			:       Union[str, Any]=None ,     )->Dict:





       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     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=13,__lowerCamelCase=7,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=99,__lowerCamelCase=16,__lowerCamelCase=2,__lowerCamelCase=4,__lowerCamelCase=4,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=32,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=0,__lowerCamelCase=0.02,):
        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 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        A__											=						self.prepare_config_and_inputs()
        return config, inputs_dict



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						20
        A__											=						model_class_name(__SCREAMING_SNAKE_CASE )

        A__											=						model.encode(inputs_dict['''input_ids'''] )

        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						20
        A__											=						model_class_name(__SCREAMING_SNAKE_CASE )

        A__											=						model.encode(inputs_dict['''input_ids'''] )

        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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	99



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        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     SCREAMING_SNAKE_CASE__ (						lowerCamelCase_				,					unittest.TestCase				,					lowerCamelCase_      ):
 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE							       =	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



 def 				UpperCamelCase			(							self ):
        A__											=						FlaxBlenderbotModelTester(self )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        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(__lowerCamelCase,__lowerCamelCase=None,**__lowerCamelCase ):
                             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 				UpperCamelCase			(							self ):
        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(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
                             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 				UpperCamelCase			(							self ):





        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 				UpperCamelCase			(							self ):
        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







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



