


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			=						'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    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : str=None      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]=None      ,					SCREAMING_SNAKE_CASE__      : Dict=None      ,					SCREAMING_SNAKE_CASE__      : Tuple=None      ,					SCREAMING_SNAKE_CASE__      : Any=None      ,					SCREAMING_SNAKE_CASE__      : str=None      ,					):
     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    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=13 ,       A_=7 ,       A_=True ,       A_=False ,       A_=99 ,       A_=16 ,       A_=2 ,       A_=4 ,       A_=4 ,       A_="gelu" ,       A_=0.1 ,       A_=0.1 ,       A_=32 ,       A_=2 ,       A_=1 ,       A_=0 ,       A_=0.02 ,       )  ->	List[str]:
         __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 _a (      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(A_ ,       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=A_ ,       )
         __UpperCamelCase   							=prepare_blenderbot_inputs_dict(A_ ,       A_ ,       A_   )
         return config, inputs_dict






    def _a (      self   )  ->	Dict:
         __UpperCamelCase							,				__UpperCamelCase   							=self.prepare_config_and_inputs()
         return config, inputs_dict






    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							=20
         __UpperCamelCase   							=model_class_name(A_   )

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

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

         __UpperCamelCase   							=model.decode(A_ ,       A_   )

         __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 _a (      self ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=20
         __UpperCamelCase   							=model_class_name(A_   )

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

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

         __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    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  9_9






    def _a (      self   )  ->	Any:
         __UpperCamelCase   							=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 ,       )

         __UpperCamelCase   							=input_ids.shape[0]
         __UpperCamelCase   							=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 _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self._get_config_and_data()
         __UpperCamelCase   							=FlaxBlenderbotForConditionalGeneration(A_   )
         __UpperCamelCase   							=lm_model(input_ids=A_   )
         __UpperCamelCase   							=(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape ,       A_   )






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






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase   							=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,       dtype=np.intaa   )
         __UpperCamelCase   							=shift_tokens_right(A_ ,       1 ,       2   )
         __UpperCamelCase   							=np.equal(A_ ,       1   ).astype(np.floataa   ).sum()
         __UpperCamelCase   							=np.equal(A_ ,       1   ).astype(np.floataa   ).sum()
         self.assertEqual(shifted.shape ,       input_ids.shape   )
         self.assertEqual(A_ ,       n_pad_before - 1   )
         self.assertTrue(np.equal(shifted[:, 0] ,       2   ).all()   )






@require_flax
class    UpperCAmelCase__	( A_       ,  unittest.TestCase       ,  A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple              =  True
    UpperCAmelCase__						:		Any              =  (
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    UpperCAmelCase__						:		str              =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






    def _a (      self   )  ->	Dict:
         __UpperCamelCase   							=FlaxBlenderbotModelTester(self   )






    def _a (      self   )  ->	Optional[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(A_ ,       A_ ,       A_   )






    def _a (      self   )  ->	Tuple:
         __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(A_ ,       A_ ,       A_   )






    def _a (      self   )  ->	Optional[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(A_ ,       A_   )
                   __UpperCamelCase   							=model_class(A_   )

                   @jax.jit
                   def encode_jitted(A_ ,       A_=None ,       **A_   ):
                        return model.encode(input_ids=A_ ,       attention_mask=A_   )

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

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

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






    def _a (      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   							=model_class(A_   )
                   __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_ ,       A_ ,       A_   ):
                        return model.decode(
                            decoder_input_ids=A_ ,       decoder_attention_mask=A_ ,       encoder_outputs=A_ ,       )

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

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

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






    @slow
    def _a (      self   )  ->	List[Any]:
         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(A_   )
              self.assertIsNotNone(A_   )






    @unittest.skipUnless(jax_device != 'cpu' ,       '3B test too slow on CPU.'   )
    @slow
    def _a (      self   )  ->	Any:
         __UpperCamelCase   							={'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
         __UpperCamelCase   							={'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

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

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

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

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









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





