


'''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
 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 _SCREAMING_SNAKE_CASE      (A  , A  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , )       ->       Union[str, Any]:






       """simple docstring"""







       if attention_mask is None:
              lowercase__      			= np.where(input_ids != config.pad_token_id  , 1  , 0       )
       if decoder_attention_mask is None:
              lowercase__      			= np.where(decoder_input_ids != config.pad_token_id  , 1  , 0       )
       if head_mask is None:
              lowercase__      			= np.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              lowercase__      			= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              lowercase__      			= 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    :    List[str]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Any=13   ,						UpperCamelCase    :    Optional[int]=7   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    Union[str, Any]=99   ,						UpperCamelCase    :    List[Any]=16   ,						UpperCamelCase    :    List[Any]=2   ,						UpperCamelCase    :    List[str]=4   ,						UpperCamelCase    :    Tuple=4   ,						UpperCamelCase    :    Dict="gelu"   ,						UpperCamelCase    :    Tuple=0.1   ,						UpperCamelCase    :    List[Any]=0.1   ,						UpperCamelCase    :    Any=32   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=1   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    List[str]=0.02   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= seq_length
             lowercase__      			= is_training
             lowercase__      			= use_labels
             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_act
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= max_position_embeddings
             lowercase__      			= eos_token_id
             lowercase__      			= pad_token_id
             lowercase__      			= bos_token_id
             lowercase__      			= initializer_range



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size )   ,						3   ,						self.vocab_size )
             lowercase__      			= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,						dtype=np.intaa ))   ,						-1 )

             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						1   ,						2 )

             lowercase__      			= 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=UpperCamelCase   ,						)
             lowercase__      			= prepare_blenderbot_inputs_dict(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
             return config, inputs_dict



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= 20
             lowercase__      			= model_class_name(UpperCamelCase )

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

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

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

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

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

             lowercase__      			= model.decode(UpperCamelCase   ,						UpperCamelCase )

             lowercase__      			= 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    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= 20
             lowercase__      			= model_class_name(UpperCamelCase )

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

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

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

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

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

             lowercase__      			= model.decode(UpperCamelCase   ,						UpperCamelCase   ,						decoder_attention_mask=UpperCamelCase )

             lowercase__      			= 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'''






      lowerCAmelCase__					:     List[Any]  					=							99



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= 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   ,						)

             lowercase__      			= input_ids.shape[0]
             lowercase__      			= 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    :    Union[str, Any] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= 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   ,						)
             lowercase__      			= FlaxBlenderbotForConditionalGeneration(UpperCamelCase )
             lowercase__      			= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,						dtype=np.intaa )
             lowercase__      			= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,						dtype=np.intaa )
             lowercase__      			= lm_model(input_ids=UpperCamelCase   ,						decoder_input_ids=UpperCamelCase )
             lowercase__      			= (*summary.shape, config.vocab_size)
             self.assertEqual(outputs['''logits'''].shape   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,						dtype=np.intaa )
             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						1   ,						2 )
             lowercase__      			= np.equal(UpperCamelCase   ,						1 ).astype(np.floataa ).sum()
             lowercase__      			= np.equal(UpperCamelCase   ,						1 ).astype(np.floataa ).sum()
             self.assertEqual(shifted.shape   ,						input_ids.shape )
             self.assertEqual(UpperCamelCase   ,						n_pad_before - 1 )
             self.assertTrue(np.equal(shifted[:, 0]   ,						2 ).all() )






@require_flax
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase  ,     lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							True
      lowerCAmelCase__					:     str  					=							(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      lowerCAmelCase__					:     List[Any]  					=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= FlaxBlenderbotModelTester(self )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= 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(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__ ):
                           lowercase__      			= self._prepare_for_class(UpperCamelCase   ,						UpperCamelCase )
                           lowercase__      			= model_class(UpperCamelCase )

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

                           with self.subTest('''JIT Enabled''' ):
                                  lowercase__      			= encode_jitted(**UpperCamelCase ).to_tuple()

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

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



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

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

                           lowercase__      			= {
                               '''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(UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Dict ):
                                  return model.decode(
                                      decoder_input_ids=UpperCamelCase   ,						decoder_attention_mask=UpperCamelCase   ,						encoder_outputs=UpperCamelCase   ,						)

                           with self.subTest('''JIT Enabled''' ):
                                  lowercase__      			= decode_jitted(**UpperCamelCase ).to_tuple()

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

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



      @slow
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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






      @unittest.skipUnless(jax_device != '''cpu'''   ,						'''3B test too slow on CPU.''' )
      @slow
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
             lowercase__      			= {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

             lowercase__      			= ['''Sam''']
             lowercase__      			= tokenizer(UpperCamelCase   ,						return_tensors='''jax''' )

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

             lowercase__      			= tokenizer.batch_decode(UpperCamelCase   ,						**UpperCamelCase )
             assert generated_txt[0].strip() == tgt_text








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




