




import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      args.pruning_method
      SCREAMING_SNAKE_CASE_     						=      args.threshold

      SCREAMING_SNAKE_CASE_     						=      args.model_name_or_path.rstrip('/'							)
      SCREAMING_SNAKE_CASE_     						=      args.target_model_path

      print(f"Load fine-pruned model from {model_name_or_path}"							)
      SCREAMING_SNAKE_CASE_     						=      torch.load(os.path.join(__UpperCAmelCase      ,      'pytorch_model.bin'							)							)
      SCREAMING_SNAKE_CASE_     						=      {}

      for name, tensor in model.items():
            if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                  SCREAMING_SNAKE_CASE_     						=      tensor
                  print(f"Copied layer {name}"							)
            elif "classifier" in name or "qa_output" in name:
                  SCREAMING_SNAKE_CASE_     						=      tensor
                  print(f"Copied layer {name}"							)
            elif "bias" in name:
                  SCREAMING_SNAKE_CASE_     						=      tensor
                  print(f"Copied layer {name}"							)
            else:
                  if pruning_method == "magnitude":
                        SCREAMING_SNAKE_CASE_     						=      MagnitudeBinarizer.apply(inputs=__UpperCAmelCase      ,      threshold=__UpperCAmelCase							)
                        SCREAMING_SNAKE_CASE_     						=      tensor * mask
                        print(f"Pruned layer {name}"							)
                  elif pruning_method == "topK":
                        if "mask_scores" in name:
                              continue
                        SCREAMING_SNAKE_CASE_     						=      name[:-6]
                        SCREAMING_SNAKE_CASE_     						=      model[f"{prefix_}mask_scores"]
                        SCREAMING_SNAKE_CASE_     						=      TopKBinarizer.apply(__UpperCAmelCase      ,      __UpperCAmelCase							)
                        SCREAMING_SNAKE_CASE_     						=      tensor * mask
                        print(f"Pruned layer {name}"							)
                  elif pruning_method == "sigmoied_threshold":
                        if "mask_scores" in name:
                              continue
                        SCREAMING_SNAKE_CASE_     						=      name[:-6]
                        SCREAMING_SNAKE_CASE_     						=      model[f"{prefix_}mask_scores"]
                        SCREAMING_SNAKE_CASE_     						=      ThresholdBinarizer.apply(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)
                        SCREAMING_SNAKE_CASE_     						=      tensor * mask
                        print(f"Pruned layer {name}"							)
                  elif pruning_method == "l0":
                        if "mask_scores" in name:
                              continue
                        SCREAMING_SNAKE_CASE_     						=      name[:-6]
                        SCREAMING_SNAKE_CASE_     						=      model[f"{prefix_}mask_scores"]
                        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      -0.1, 1.1
                        SCREAMING_SNAKE_CASE_     						=      torch.sigmoid(__UpperCAmelCase							)
                        SCREAMING_SNAKE_CASE_     						=      s * (r - l) + l
                        SCREAMING_SNAKE_CASE_     						=      s_bar.clamp(min=0.0      ,      max=1.0							)
                        SCREAMING_SNAKE_CASE_     						=      tensor * mask
                        print(f"Pruned layer {name}"							)
                  else:
                        raise ValueError('Unknown pruning method'							)

      if target_model_path is None:
            SCREAMING_SNAKE_CASE_     						=      os.path.join(
                os.path.dirname(__UpperCAmelCase							)      ,      f"bertarized_{os.path.basename(__UpperCAmelCase							)}"							)

      if not os.path.isdir(__UpperCAmelCase							):
            shutil.copytree(__UpperCAmelCase      ,      __UpperCAmelCase							)
            print(f"\nCreated folder {target_model_path}"							)

      torch.save(__UpperCAmelCase      ,      os.path.join(__UpperCAmelCase      ,      'pytorch_model.bin'							)							)
      print('\nPruned model saved! See you later!'							)


if __name__ == "__main__":
   lowerCamelCase__			:       List[str]         =							argparse.ArgumentParser()

   parser.add_argument(
       '--pruning_method',
       choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
       type=str,
       required=True,
       help=(
           'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
           ' sigmoied_threshold = Soft movement pruning)'
       ),
   )
   parser.add_argument(
       '--threshold',
       type=float,
       required=False,
       help=(
           'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
           'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
           'Not needed for `l0`'
       ),
   )
   parser.add_argument(
       '--model_name_or_path',
       type=str,
       required=True,
       help='Folder containing the model that was previously fine-pruned',
   )
   parser.add_argument(
       '--target_model_path',
       default=None,
       type=str,
       required=False,
       help='Folder containing the model that was previously fine-pruned',
   )

   lowerCamelCase__			:       Tuple         =							parser.parse_args()

   main(args)





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
   lowerCamelCase__			:       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  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Dict=None      ,      __UpperCAmelCase						:     List[str]=None      ,      __UpperCAmelCase						:     Optional[Any]=None      ,      __UpperCAmelCase						:     Optional[Any]=None      ,      __UpperCAmelCase						:     Any=None      ,      __UpperCAmelCase						:     Union[str, Any]=None      ,      ) ->			List[Any]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.where(input_ids != config.pad_token_id      ,      1      ,      0							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.where(decoder_input_ids != config.pad_token_id      ,      1      ,      0							)
      if head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_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 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Tuple    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Union[str, Any]=13    ,    _lowerCAmelCase  :		List[Any]=7    ,    _lowerCAmelCase  :		Optional[int]=True    ,    _lowerCAmelCase  :		List[Any]=False    ,    _lowerCAmelCase  :		Union[str, Any]=99    ,    _lowerCAmelCase  :		List[str]=16    ,    _lowerCAmelCase  :		List[str]=2    ,    _lowerCAmelCase  :		Union[str, Any]=4    ,    _lowerCAmelCase  :		str=4    ,    _lowerCAmelCase  :		List[str]="gelu"    ,    _lowerCAmelCase  :		Any=0.1    ,    _lowerCAmelCase  :		List[str]=0.1    ,    _lowerCAmelCase  :		str=32    ,    _lowerCAmelCase  :		str=2    ,    _lowerCAmelCase  :		Union[str, Any]=1    ,    _lowerCAmelCase  :		Any=0    ,    _lowerCAmelCase  :		Union[str, Any]=0.02    ,    ):
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      seq_length
       SCREAMING_SNAKE_CASE_     						=      is_training
       SCREAMING_SNAKE_CASE_     						=      use_labels
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      intermediate_size
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      max_position_embeddings
       SCREAMING_SNAKE_CASE_     						=      eos_token_id
       SCREAMING_SNAKE_CASE_     						=      pad_token_id
       SCREAMING_SNAKE_CASE_     						=      bos_token_id
       SCREAMING_SNAKE_CASE_     						=      initializer_range



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

       SCREAMING_SNAKE_CASE_     						=      shift_tokens_right(_lowerCAmelCase    ,    1    ,    2						)

       SCREAMING_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=_lowerCAmelCase    ,    )
       SCREAMING_SNAKE_CASE_     						=      prepare_blenderbot_inputs_dict(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      20
       SCREAMING_SNAKE_CASE_     						=      model_class_name(_lowerCAmelCase						)

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

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

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

       SCREAMING_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)    ,    )
       SCREAMING_SNAKE_CASE_     						=      model.decode(
           decoder_input_ids[:, :-1]    ,    _lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase    ,    past_key_values=_lowerCAmelCase    ,    decoder_position_ids=_lowerCAmelCase    ,    )

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

       SCREAMING_SNAKE_CASE_     						=      model.decode(_lowerCAmelCase    ,    _lowerCAmelCase						)

       SCREAMING_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 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_     						=      20
       SCREAMING_SNAKE_CASE_     						=      model_class_name(_lowerCAmelCase						)

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

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

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

       SCREAMING_SNAKE_CASE_     						=      model.init_cache(decoder_input_ids.shape[0]    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       SCREAMING_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)    ,    )

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

       SCREAMING_SNAKE_CASE_     						=      model.decode(_lowerCAmelCase    ,    _lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase						)

       SCREAMING_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 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   99



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_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    ,    )

       SCREAMING_SNAKE_CASE_     						=      input_ids.shape[0]
       SCREAMING_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 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self._get_config_and_data()
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotForConditionalGeneration(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      lm_model(input_ids=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_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    ,    )
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotForConditionalGeneration(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      lm_model(input_ids=_lowerCAmelCase    ,    decoder_input_ids=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape    ,    _lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      shift_tokens_right(_lowerCAmelCase    ,    1    ,    2						)
       SCREAMING_SNAKE_CASE_     						=      np.equal(_lowerCAmelCase    ,    1						).astype(np.floataa						).sum()
       SCREAMING_SNAKE_CASE_     						=      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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase	,       _SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   True
 lowercase_       =   (
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase_       =   (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotModelTester(self						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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__						):
                   SCREAMING_SNAKE_CASE_     						=      self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)

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

                   with self.subTest('JIT Enabled'						):
                         SCREAMING_SNAKE_CASE_     						=      encode_jitted(**_lowerCAmelCase						).to_tuple()

                   with self.subTest('JIT Disabled'						):
                         with jax.disable_jit():
                               SCREAMING_SNAKE_CASE_     						=      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  :		int						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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__						):
                   SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_     						=      model.encode(inputs_dict['input_ids']    ,    inputs_dict['attention_mask']						)

                   SCREAMING_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(_lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		str						):
                         return model.decode(
                             decoder_input_ids=_lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase    ,    encoder_outputs=_lowerCAmelCase    ,    )

                   with self.subTest('JIT Enabled'						):
                         SCREAMING_SNAKE_CASE_     						=      decode_jitted(**_lowerCAmelCase						).to_tuple()

                   with self.subTest('JIT Disabled'						):
                         with jax.disable_jit():
                               SCREAMING_SNAKE_CASE_     						=      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  :		str						):
       for model_class_name in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     						=      model_class_name.from_pretrained('facebook/blenderbot-400M-distill'						)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             SCREAMING_SNAKE_CASE_     						=      np.ones((1, 1)						) * model.config.eos_token_id
             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)




 @unittest.skipUnless(jax_device != 'cpu'    ,    '3B test too slow on CPU.'						)
 @slow
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
       SCREAMING_SNAKE_CASE_     						=      {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

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

       SCREAMING_SNAKE_CASE_     						=      ['Sam']
       SCREAMING_SNAKE_CASE_     						=      tokenizer(_lowerCAmelCase    ,    return_tensors='jax'						)

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

       SCREAMING_SNAKE_CASE_     						=      tokenizer.batch_decode(_lowerCAmelCase    ,    **_lowerCAmelCase						)
       assert generated_txt[0].strip() == tgt_text