



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

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					parent
    _snake_case    :  Optional[Any]					=					batch_size
    _snake_case    :  Union[str, Any]					=					seq_length
    _snake_case    :  List[Any]					=					is_training
    _snake_case    :  Tuple					=					use_labels
    _snake_case    :  List[str]					=					vocab_size
    _snake_case    :  int					=					hidden_size
    _snake_case    :  List[str]					=					num_hidden_layers
    _snake_case    :  int					=					num_attention_heads
    _snake_case    :  int					=					intermediate_size
    _snake_case    :  Tuple					=					hidden_act
    _snake_case    :  Dict					=					hidden_dropout_prob
    _snake_case    :  int					=					attention_probs_dropout_prob
    _snake_case    :  int					=					max_position_embeddings
    _snake_case    :  str					=					eos_token_id
    _snake_case    :  int					=					pad_token_id
    _snake_case    :  List[Any]					=					bos_token_id
    _snake_case    :  int					=					initializer_range

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

    """simple docstring"""







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

    _snake_case    :  List[str]					=					shift_tokens_right(lowerCAmelCase				,		1				,		2)

    _snake_case    :  Tuple					=					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				,		)
    _snake_case    :  str					=					prepare_blenderbot_inputs_dict(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
    return config, inputs_dict

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

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					self.prepare_config_and_inputs()
    return config, inputs_dict

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Optional[Any])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					20
    _snake_case    :  List[str]					=					model_class_name(lowerCAmelCase)

    _snake_case    :  Optional[Any]					=					model.encode(inputs_dict["""input_ids"""])

    _snake_case	,    _snake_case    :  List[str]					=					(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    _snake_case    :  Tuple					=					model.init_cache(decoder_input_ids.shape[0]				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,		dtype="""i4""")

    _snake_case    :  Dict					=					jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]				,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,		)
    _snake_case    :  str					=					model.decode(
        decoder_input_ids[:, :-1]				,		lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase				,		past_key_values=lowerCAmelCase				,		decoder_position_ids=lowerCAmelCase				,		)

    _snake_case    :  Optional[Any]					=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,		dtype="""i4""")
    _snake_case    :  Dict					=					model.decode(
        decoder_input_ids[:, -1:]				,		lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase				,		past_key_values=outputs_cache.past_key_values				,		decoder_position_ids=lowerCAmelCase				,		)

    _snake_case    :  Any					=					model.decode(lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  List[str]					=					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							:			Dict				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					20
    _snake_case    :  Any					=					model_class_name(lowerCAmelCase)

    _snake_case    :  Tuple					=					model.encode(inputs_dict["""input_ids"""])

    _snake_case	,    _snake_case    :  str					=					(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

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

    _snake_case    :  List[Any]					=					model.init_cache(decoder_input_ids.shape[0]				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]				,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,		)

    _snake_case    :  Union[str, Any]					=					model.decode(
        decoder_input_ids[:, :-1]				,		lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase				,		past_key_values=lowerCAmelCase				,		decoder_position_ids=lowerCAmelCase				,		)
    _snake_case    :  Tuple					=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,		dtype="""i4""")
    _snake_case    :  List[Any]					=					model.decode(
        decoder_input_ids[:, -1:]				,		lowerCAmelCase				,		past_key_values=outputs_cache.past_key_values				,		decoder_attention_mask=lowerCAmelCase				,		decoder_position_ids=lowerCAmelCase				,		)

    _snake_case    :  Union[str, Any]					=					model.decode(lowerCAmelCase				,		lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase)

    _snake_case    :  str					=					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'''






   snake_case_					: str          =		99

   def      UpperCamelCase_  (					self							:			List[Any])				->				Any:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					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				,		)

    _snake_case    :  Dict					=					input_ids.shape[0]
    _snake_case    :  Dict					=					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							:			Optional[Any])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case	,    _snake_case	,    _snake_case    :  str					=					self._get_config_and_data()
    _snake_case    :  Tuple					=					FlaxBlenderbotForConditionalGeneration(lowerCAmelCase)
    _snake_case    :  Dict					=					lm_model(input_ids=lowerCAmelCase)
    _snake_case    :  List[str]					=					(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape				,		lowerCAmelCase)

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

    """simple docstring"""







    _snake_case    :  Tuple					=					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				,		)
    _snake_case    :  str					=					FlaxBlenderbotForConditionalGeneration(lowerCAmelCase)
    _snake_case    :  int					=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,		dtype=np.intaa)
    _snake_case    :  Union[str, Any]					=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,		dtype=np.intaa)
    _snake_case    :  Dict					=					lm_model(input_ids=lowerCAmelCase				,		decoder_input_ids=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					(*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape				,		lowerCAmelCase)


   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  Any					=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,		dtype=np.intaa)
    _snake_case    :  List[str]					=					shift_tokens_right(lowerCAmelCase				,		1				,		2)
    _snake_case    :  Optional[Any]					=					np.equal(lowerCAmelCase				,		1).astype(np.floataa).sum()
    _snake_case    :  int					=					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						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase					,SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		True
   snake_case_					: Tuple          =		(
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   snake_case_					: int          =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

   def      UpperCamelCase_  (					self							:			List[Any])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					FlaxBlenderbotModelTester(self)

   def      UpperCamelCase_  (					self							:			Any)				->				Any:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Union[str, Any]					=					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      UpperCamelCase_  (					self							:			Dict)				->				List[Any]:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Optional[int]					=					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      UpperCamelCase_  (					self							:			Any)				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
      _snake_case    :  str					=					self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase)
      _snake_case    :  int					=					model_class(lowerCAmelCase)

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

      with self.subTest("""JIT Enabled"""):
       _snake_case    :  Any					=					encode_jitted(**lowerCAmelCase).to_tuple()

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

    """simple docstring"""







    _snake_case	,    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
      _snake_case    :  int					=					model_class(lowerCAmelCase)
      _snake_case    :  List[Any]					=					model.encode(inputs_dict["""input_ids"""]				,		inputs_dict["""attention_mask"""])

      _snake_case    :  List[str]					=					{
          """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							:			Optional[Any]				,		lowerCAmelCase							:			List[Any]):
       return model.decode(
           decoder_input_ids=lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase				,		encoder_outputs=lowerCAmelCase				,		)

      with self.subTest("""JIT Enabled"""):
       _snake_case    :  Optional[Any]					=					decode_jitted(**lowerCAmelCase).to_tuple()

      with self.subTest("""JIT Disabled"""):
       with jax.disable_jit():
        _snake_case    :  int					=					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      UpperCamelCase_  (					self							:			Tuple)				->				Dict:

    """simple docstring"""







    for model_class_name in self.all_model_classes:
     _snake_case    :  Union[str, Any]					=					model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     _snake_case    :  Dict					=					np.ones((1, 1)) * model.config.eos_token_id
     _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)


   @unittest.skipUnless(jax_device != """cpu"""				,		"""3B test too slow on CPU.""")
   @slow
   def      UpperCamelCase_  (					self							:			Tuple)				->				Dict:

    """simple docstring"""







    _snake_case    :  Dict					=					{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
    _snake_case    :  Tuple					=					{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

    _snake_case    :  Tuple					=					FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""				,		from_pt=lowerCAmelCase)
    _snake_case    :  Any					=					BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")

    _snake_case    :  Tuple					=					["""Sam"""]
    _snake_case    :  Any					=					tokenizer(lowerCAmelCase				,		return_tensors="""jax""")

    _snake_case    :  Tuple					=					model.generate(**lowerCAmelCase				,		**lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					"""Sam is a great name. It means \"sun\" in Gaelic."""

    _snake_case    :  Optional[int]					=					tokenizer.batch_decode(lowerCAmelCase				,		**lowerCAmelCase)
    assert generated_txt[0].strip() == tgt_text





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()
