





'''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
    a__				:				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 							lowercase__  (  __lowercase		:    Tuple							,    __lowercase		:    Optional[int]							,    __lowercase		:    Any=None							,    __lowercase		:    Optional[Any]=None							,    __lowercase		:    Dict=None							,    __lowercase		:    List[str]=None							,    __lowercase		:    Any=None							,    __lowercase		:    Tuple=None							,    )					-> Tuple:



       """simple docstring"""






       if attention_mask is None:
              __UpperCamelCase			=    np.where(input_ids != config.pad_token_id							,    1							,    0	)
       if decoder_attention_mask is None:
              __UpperCamelCase			=    np.where(decoder_input_ids != config.pad_token_id							,    1							,    0	)
       if head_mask is None:
              __UpperCamelCase			=    np.ones((config.encoder_layers, config.encoder_attention_heads)	)
       if decoder_head_mask is None:
              __UpperCamelCase			=    np.ones((config.decoder_layers, config.decoder_attention_heads)	)
       if cross_attn_head_mask is None:
              __UpperCamelCase			=    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			:				int				,      __A			:				Optional[Any]				,      __A			:				List[str]=1_3				,      __A			:				str=7				,      __A			:				Optional[Any]=True				,      __A			:				Tuple=False				,      __A			:				Tuple=9_9				,      __A			:				Dict=1_6				,      __A			:				List[str]=2				,      __A			:				int=4				,      __A			:				Tuple=4				,      __A			:				Dict="gelu"				,      __A			:				Optional[Any]=0.1				,      __A			:				Union[str, Any]=0.1				,      __A			:				Dict=3_2				,      __A			:				str=2				,      __A			:				Tuple=1				,      __A			:				Tuple=0				,      __A			:				List[Any]=0.02				,      ):
        __UpperCamelCase			=    parent
        __UpperCamelCase			=    batch_size
        __UpperCamelCase			=    seq_length
        __UpperCamelCase			=    is_training
        __UpperCamelCase			=    use_labels
        __UpperCamelCase			=    vocab_size
        __UpperCamelCase			=    hidden_size
        __UpperCamelCase			=    num_hidden_layers
        __UpperCamelCase			=    num_attention_heads
        __UpperCamelCase			=    intermediate_size
        __UpperCamelCase			=    hidden_act
        __UpperCamelCase			=    hidden_dropout_prob
        __UpperCamelCase			=    attention_probs_dropout_prob
        __UpperCamelCase			=    max_position_embeddings
        __UpperCamelCase			=    eos_token_id
        __UpperCamelCase			=    pad_token_id
        __UpperCamelCase			=    bos_token_id
        __UpperCamelCase			=    initializer_range






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

        __UpperCamelCase			=    shift_tokens_right(lowerCAmelCase__				,      1				,      2      )

        __UpperCamelCase			=    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=lowerCAmelCase__				,      )
        __UpperCamelCase			=    prepare_blenderbot_inputs_dict(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__      )
        return config, inputs_dict






 def 			_lowerCamelCase   (			self			:				int      ):
        __UpperCamelCase			,		__UpperCamelCase			=    self.prepare_config_and_inputs()
        return config, inputs_dict






 def 			_lowerCamelCase   (			self			:				str				,      __A			:				List[str]				,      __A			:				Tuple				,      __A			:				Tuple      ):
        __UpperCamelCase			=    2_0
        __UpperCamelCase			=    model_class_name(lowerCAmelCase__      )

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

        __UpperCamelCase			,		__UpperCamelCase			=    (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __UpperCamelCase			=    model.init_cache(decoder_input_ids.shape[0]				,      lowerCAmelCase__				,      lowerCAmelCase__      )
        __UpperCamelCase			=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,      dtype='i4'      )

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

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

        __UpperCamelCase			=    model.decode(lowerCAmelCase__				,      lowerCAmelCase__      )

        __UpperCamelCase			=    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			:				int				,      __A			:				Optional[Any]				,      __A			:				Optional[int]				,      __A			:				Optional[int]      ):
        __UpperCamelCase			=    2_0
        __UpperCamelCase			=    model_class_name(lowerCAmelCase__      )

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

        __UpperCamelCase			,		__UpperCamelCase			=    (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

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

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

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

        __UpperCamelCase			=    model.decode(lowerCAmelCase__				,      lowerCAmelCase__				,      decoder_attention_mask=lowerCAmelCase__      )

        __UpperCamelCase			=    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_      :   List[Any]     						=99






 def 			_lowerCamelCase   (			self			:				Dict      ):
        __UpperCamelCase			=    np.array(
            [
                [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
            ]				,      dtype=np.intaa				,      )

        __UpperCamelCase			=    input_ids.shape[0]
        __UpperCamelCase			=    BlenderbotConfig(
            vocab_size=self.vocab_size				,      d_model=2_4				,      encoder_layers=2				,      decoder_layers=2				,      encoder_attention_heads=2				,      decoder_attention_heads=2				,      encoder_ffn_dim=3_2				,      decoder_ffn_dim=3_2				,      max_position_embeddings=4_8				,      eos_token_id=2				,      pad_token_id=1				,      bos_token_id=0				,      )
        return config, input_ids, batch_size






 def 			_lowerCamelCase   (			self			:				Dict      ):
        __UpperCamelCase			,		__UpperCamelCase			,		__UpperCamelCase			=    self._get_config_and_data()
        __UpperCamelCase			=    FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__      )
        __UpperCamelCase			=    lm_model(input_ids=lowerCAmelCase__      )
        __UpperCamelCase			=    (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape				,      lowerCAmelCase__      )






 def 			_lowerCamelCase   (			self			:				Any      ):
        __UpperCamelCase			=    BlenderbotConfig(
            vocab_size=self.vocab_size				,      d_model=1_4				,      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=4_8				,      )
        __UpperCamelCase			=    FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__      )
        __UpperCamelCase			=    np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,      dtype=np.intaa      )
        __UpperCamelCase			=    np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,      dtype=np.intaa      )
        __UpperCamelCase			=    lm_model(input_ids=lowerCAmelCase__				,      decoder_input_ids=lowerCAmelCase__      )
        __UpperCamelCase			=    (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape				,      lowerCAmelCase__      )






 def 			_lowerCamelCase   (			self			:				List[str]      ):
        __UpperCamelCase			=    np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,      dtype=np.intaa      )
        __UpperCamelCase			=    shift_tokens_right(lowerCAmelCase__				,      1				,      2      )
        __UpperCamelCase			=    np.equal(lowerCAmelCase__				,      1      ).astype(np.floataa      ).sum()
        __UpperCamelCase			=    np.equal(lowerCAmelCase__				,      1      ).astype(np.floataa      ).sum()
        self.assertEqual(shifted.shape				,      input_ids.shape      )
        self.assertEqual(lowerCAmelCase__				,      n_pad_before - 1      )
        self.assertTrue(np.equal(shifted[:, 0]				,      2      ).all()      )






@require_flax
class 	snake_case	(       __lowerCamelCase     ,			unittest.TestCase     ,			__lowerCamelCase			):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_      :   Dict     						=True
 SCREAMING_SNAKE_CASE_      :   str     						=(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_      :   Tuple     						=(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






 def 			_lowerCamelCase   (			self			:				Union[str, Any]      ):
        __UpperCamelCase			=    FlaxBlenderbotModelTester(self      )






 def 			_lowerCamelCase   (			self			:				List[str]      ):
        __UpperCamelCase			,		__UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__      )






 def 			_lowerCamelCase   (			self			:				Union[str, Any]      ):
        __UpperCamelCase			,		__UpperCamelCase			=    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(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__      )






 def 			_lowerCamelCase   (			self			:				Union[str, Any]      ):
        __UpperCamelCase			,		__UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                      __UpperCamelCase			=    self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__      )
                      __UpperCamelCase			=    model_class(lowerCAmelCase__      )

                      @jax.jit
                      def encode_jitted(__A			:				Union[str, Any]				,      __A			:				Dict=None				,      **__A			:				Optional[Any]      ):
                             return model.encode(input_ids=lowerCAmelCase__				,      attention_mask=lowerCAmelCase__      )

                      with self.subTest('JIT Enabled'      ):
                             __UpperCamelCase			=    encode_jitted(**lowerCAmelCase__      ).to_tuple()

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

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






 def 			_lowerCamelCase   (			self			:				Dict      ):
        __UpperCamelCase			,		__UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                      __UpperCamelCase			=    model_class(lowerCAmelCase__      )
                      __UpperCamelCase			=    model.encode(inputs_dict['input_ids']				,      inputs_dict['attention_mask']      )

                      __UpperCamelCase			=    {
                          '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(__A			:				List[Any]				,      __A			:				List[Any]				,      __A			:				List[Any]      ):
                             return model.decode(
                                 decoder_input_ids=lowerCAmelCase__				,      decoder_attention_mask=lowerCAmelCase__				,      encoder_outputs=lowerCAmelCase__				,      )

                      with self.subTest('JIT Enabled'      ):
                             __UpperCamelCase			=    decode_jitted(**lowerCAmelCase__      ).to_tuple()

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

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






 @slow
 def 			_lowerCamelCase   (			self			:				Dict      ):




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






 @unittest.skipUnless(jax_device != 'cpu'				,      '3B test too slow on CPU.'      )
 @slow
 def 			_lowerCamelCase   (			self			:				int      ):
        __UpperCamelCase			=    {'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
        __UpperCamelCase			=    {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

        __UpperCamelCase			=    FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'				,      from_pt=lowerCAmelCase__      )
        __UpperCamelCase			=    BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'      )

        __UpperCamelCase			=    ['Sam']
        __UpperCamelCase			=    tokenizer(lowerCAmelCase__				,      return_tensors='jax'      )

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

        __UpperCamelCase			=    tokenizer.batch_decode(lowerCAmelCase__				,      **lowerCAmelCase__      )
        assert generated_txt[0].strip() == tgt_text







'''simple docstring'''

import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



