





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       :   int					= '''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   __lowerCamelCase   (				__a  :str       ,	__a  :Dict       ,	__a  :Optional[int]=None       ,	__a  :Tuple=None       ,	__a  :Optional[int]=None       ,	__a  :Optional[int]=None       ,	__a  :Any=None       ,	__a  :Optional[Any]=None       ,	)						->							Union[str, Any]:






		"""simple docstring"""

		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     A     :
			'''simple docstring'''




			def __init__(     self  :       Tuple				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       Optional[Any]=13				,      __lowerCAmelCase  :       Dict=7				,      __lowerCAmelCase  :       Dict=True				,      __lowerCAmelCase  :       str=False				,      __lowerCAmelCase  :       Optional[Any]=99				,      __lowerCAmelCase  :       List[Any]=16				,      __lowerCAmelCase  :       List[Any]=2				,      __lowerCAmelCase  :       List[str]=4				,      __lowerCAmelCase  :       Optional[Any]=4				,      __lowerCAmelCase  :       Optional[Any]="gelu"				,      __lowerCAmelCase  :       Optional[int]=0.1				,      __lowerCAmelCase  :       List[Any]=0.1				,      __lowerCAmelCase  :       Tuple=32				,      __lowerCAmelCase  :       Optional[int]=2				,      __lowerCAmelCase  :       Union[str, Any]=1				,      __lowerCAmelCase  :       List[str]=0				,      __lowerCAmelCase  :       Optional[int]=0.0_2				,      )      ->      List[Any]:







					"""simple docstring"""






					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   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					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(__lowerCAmelCase				,      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=__lowerCAmelCase				,      )
					A__     =  prepare_blenderbot_inputs_dict(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase  )
					return config, inputs_dict






			def   a_			(     self  :       Optional[int]  )      ->      List[Any]:







					"""simple docstring"""






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






			def   a_			(     self  :       str				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       Any  )      ->      Dict:







					"""simple docstring"""






					A__     =  20
					A__     =  model_class_name(__lowerCAmelCase  )

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

					A__ ,     A__     =  (
					    inputs_dict["""decoder_input_ids"""],
					    inputs_dict["""decoder_attention_mask"""],
					)

					A__     =  model.init_cache(decoder_input_ids.shape[0]				,      __lowerCAmelCase				,      __lowerCAmelCase  )
					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]				,      __lowerCAmelCase				,      decoder_attention_mask=__lowerCAmelCase				,      past_key_values=__lowerCAmelCase				,      decoder_position_ids=__lowerCAmelCase				,      )

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

					A__     =  model.decode(__lowerCAmelCase				,      __lowerCAmelCase  )

					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   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  20
					A__     =  model_class_name(__lowerCAmelCase  )

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

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

					A__     =  model.decode(__lowerCAmelCase				,      __lowerCAmelCase				,      decoder_attention_mask=__lowerCAmelCase  )

					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     A     (unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       99






			def   a_			(     self  :       List[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					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   a_			(     self  :       Dict  )      ->      Optional[Any]:







					"""simple docstring"""






					A__ ,     A__ ,     A__     =  self._get_config_and_data()
					A__     =  FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase  )
					A__     =  lm_model(input_ids=__lowerCAmelCase  )
					A__     =  (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape				,      __lowerCAmelCase  )






			def   a_			(     self  :       Dict  )      ->      List[str]:







					"""simple docstring"""






					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(__lowerCAmelCase  )
					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=__lowerCAmelCase				,      decoder_input_ids=__lowerCAmelCase  )
					A__     =  (*summary.shape, config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape				,      __lowerCAmelCase  )







			def   a_			(     self  :       Any  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,      dtype=np.intaa  )
					A__     =  shift_tokens_right(__lowerCAmelCase				,      1				,      2  )
					A__     =  np.equal(__lowerCAmelCase				,      1  ).astype(np.floataa  ).sum()
					A__     =  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     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase  ,	SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			int							     =       True
			__lowerCamelCase      :			Any							     =       (
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			__lowerCamelCase      :			Dict							     =       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






			def   a_			(     self  :       List[str]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  FlaxBlenderbotModelTester(self  )






			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__ ,     A__     =  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   a_			(     self  :       Optional[int]  )      ->      Any:







					"""simple docstring"""






					A__ ,     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(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase  )






			def   a_			(     self  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






					A__ ,     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(__lowerCAmelCase				,      __lowerCAmelCase  )
									A__     =  model_class(__lowerCAmelCase  )

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

									with self.subTest("""JIT Enabled"""  ):
											A__     =  encode_jitted(**__lowerCAmelCase  ).to_tuple()

									with self.subTest("""JIT Disabled"""  ):
											with jax.disable_jit():
													A__     =  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   a_			(     self  :       List[str]  )      ->      List[str]:







					"""simple docstring"""






					A__ ,     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(__lowerCAmelCase  )
									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  :       str				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       List[str]  ):
											return model.decode(
											    decoder_input_ids=__lowerCAmelCase				,      decoder_attention_mask=__lowerCAmelCase				,      encoder_outputs=__lowerCAmelCase				,      )

									with self.subTest("""JIT Enabled"""  ):
											A__     =  decode_jitted(**__lowerCAmelCase  ).to_tuple()

									with self.subTest("""JIT Disabled"""  ):
											with jax.disable_jit():
													A__     =  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   a_			(     self  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






					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(__lowerCAmelCase  )
							self.assertIsNotNone(__lowerCAmelCase  )







			@unittest.skipUnless(jax_device != """cpu"""				,      """3B test too slow on CPU."""  )
			@slow
			def   a_			(     self  :       Optional[int]  )      ->      List[str]:







					"""simple docstring"""






					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=__lowerCAmelCase  )
					A__     =  BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""  )

					A__     =  ["""Sam"""]
					A__     =  tokenizer(__lowerCAmelCase				,      return_tensors="""jax"""  )

					A__     =  model.generate(**__lowerCAmelCase				,      **__lowerCAmelCase  )
					A__     =  """Sam is a great name. It means \"sun\" in Gaelic."""

					A__     =  tokenizer.batch_decode(__lowerCAmelCase				,      **__lowerCAmelCase  )
					assert generated_txt[0].strip() == tgt_text










import numpy as np







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return 1 / (1 + np.exp(-vector       ))







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return vector * sigmoid(1.702 * vector       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



