




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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_						=			'platform'

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		)			->							str:





     '''simple docstring'''


     if attention_mask is None:
          lowercase :       Union[str, Any]       =			np.where(input_ids != config.pad_token_id			,		1			,		0   )
     if decoder_attention_mask is None:
          lowercase :       List[Any]       =			np.where(decoder_input_ids != config.pad_token_id			,		1			,		0   )
     if head_mask is None:
          lowercase :       str       =			np.ones((config.encoder_layers, config.encoder_attention_heads)   )
     if decoder_head_mask is None:
          lowercase :       Tuple       =			np.ones((config.decoder_layers, config.decoder_attention_heads)   )
     if cross_attn_head_mask is None:
          lowercase :       Optional[int]       =			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 :






  def __init__(  self		:     List[Any]				,						_A		:     List[Any]				,						_A		:     Any=13				,						_A		:     Optional[int]=7				,						_A		:     List[str]=True				,						_A		:     Optional[Any]=False				,						_A		:     Optional[int]=99				,						_A		:     List[str]=16				,						_A		:     Any=2				,						_A		:     Tuple=4				,						_A		:     Dict=4				,						_A		:     List[str]="gelu"				,						_A		:     str=0.1				,						_A		:     List[str]=0.1				,						_A		:     Dict=32				,						_A		:     List[Any]=2				,						_A		:     Any=1				,						_A		:     Union[str, Any]=0				,						_A		:     Dict=0.02				,						)		->   int:






       """simple docstring"""







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






  def  __a      (  self		:     Dict				)		->   List[Any]:






       """simple docstring"""







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

       lowercase :       Any       =			shift_tokens_right(_A				,						1				,						2				)

       lowercase :       Dict       =			BlenderbotSmallConfig(
           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				,						)
       lowercase :       str       =			prepare_blenderbot_inputs_dict(_A				,						_A				,						_A				)
       return config, inputs_dict






  def  __a      (  self		:     List[Any]				)		->   Dict:






       """simple docstring"""







       lowercase      ,	lowercase :       Optional[int]       =			self.prepare_config_and_inputs()
       return config, inputs_dict






  def  __a      (  self		:     Optional[Any]				,						_A		:     str				,						_A		:     Dict				,						_A		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Tuple       =			20
       lowercase :       List[Any]       =			model_class_name(_A				)

       lowercase :       Union[str, Any]       =			model.encode(inputs_dict['''input_ids''']				)

       lowercase      ,	lowercase :       Union[str, Any]       =			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       lowercase :       Union[str, Any]       =			model.init_cache(decoder_input_ids.shape[0]				,						_A				,						_A				)
       lowercase :       Tuple       =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,						dtype='''i4'''				)

       lowercase :       Tuple       =			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)
       lowercase :       Optional[Any]       =			model.decode(
           decoder_input_ids[:, :-1]				,						_A				,						decoder_attention_mask=_A				,						past_key_values=_A				,						decoder_position_ids=_A				,						)

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

       lowercase :       Optional[Any]       =			model.decode(_A				,						_A				)

       lowercase :       Tuple       =			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		:     Dict				,						_A		:     List[str]				,						_A		:     str				,						_A		:     Any				)		->   List[str]:






       """simple docstring"""







       lowercase :       List[str]       =			20
       lowercase :       Union[str, Any]       =			model_class_name(_A				)

       lowercase :       Dict       =			model.encode(inputs_dict['''input_ids''']				)

       lowercase      ,	lowercase :       Union[str, Any]       =			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       lowercase :       List[str]       =			jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
           ]				,						axis=-1				,						)

       lowercase :       Any       =			model.init_cache(decoder_input_ids.shape[0]				,						_A				,						_A				)
       lowercase :       List[str]       =			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)

       lowercase :       Optional[Any]       =			model.decode(
           decoder_input_ids[:, :-1]				,						_A				,						decoder_attention_mask=_A				,						past_key_values=_A				,						decoder_position_ids=_A				,						)
       lowercase :       str       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype='''i4'''				)
       lowercase :       List[str]       =			model.decode(
           decoder_input_ids[:, -1:]				,						_A				,						past_key_values=outputs_cache.past_key_values				,						decoder_attention_mask=_A				,						decoder_position_ids=_A				,						)

       lowercase :       int       =			model.decode(_A				,						_A				,						decoder_attention_mask=_A				)

       lowercase :       Union[str, Any]       =			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				):
  _UpperCamelCase       :      Any			=      9_9






  def  __a      (  self		:     Optional[Any]				)		->   str:






       """simple docstring"""







       lowercase :       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				,						)

       lowercase :       int       =			input_ids.shape[0]
       lowercase :       int       =			BlenderbotSmallConfig(
           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		:     Union[str, Any]				)		->   List[str]:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       int       =			self._get_config_and_data()
       lowercase :       Optional[int]       =			FlaxBlenderbotSmallForConditionalGeneration(_A				)
       lowercase :       List[str]       =			lm_model(input_ids=_A				)
       lowercase :       List[Any]       =			(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape				,						_A				)






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






       """simple docstring"""







       lowercase :       List[Any]       =			BlenderbotSmallConfig(
           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 :       List[str]       =			FlaxBlenderbotSmallForConditionalGeneration(_A				)
       lowercase :       List[str]       =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,						dtype=np.intaa				)
       lowercase :       Union[str, Any]       =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,						dtype=np.intaa				)
       lowercase :       Optional[int]       =			lm_model(input_ids=_A				,						decoder_input_ids=_A				)
       lowercase :       Optional[Any]       =			(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape				,						_A				)






  def  __a      (  self		:     Dict				)		->   Dict:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,						dtype=np.intaa				)
       lowercase :       Union[str, Any]       =			shift_tokens_right(_A				,						1				,						2				)
       lowercase :       Any       =			np.equal(_A				,						1				).astype(np.floataa				).sum()
       lowercase :       str       =			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 						_A (  _lowerCamelCase      ,       unittest.TestCase      ,       _lowerCamelCase				):
  _UpperCamelCase       :      Optional[int]			=      True
  _UpperCamelCase       :      Tuple			=      (
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  _UpperCamelCase       :      int			=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       Tuple       =			FlaxBlenderbotSmallModelTester(self				)






  def  __a      (  self		:     int				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       Tuple       =			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		:     str				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       List[str]       =			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		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase      ,	lowercase :       Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       List[Any]       =			self._prepare_for_class(_A				,						_A				)
                 lowercase :       Dict       =			model_class(_A				)

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

                 with self.subTest('''JIT Enabled'''				):
                      lowercase :       List[str]       =			encode_jitted(**_A				).to_tuple()

                 with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                           lowercase :       Optional[Any]       =			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		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase      ,	lowercase :       Any       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       List[str]       =			model_class(_A				)
                 lowercase :       Dict       =			model.encode(inputs_dict['''input_ids''']				,						inputs_dict['''attention_mask''']				)

                 lowercase :       Any       =			{
                     '''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		:     str				,						_A		:     Union[str, Any]				,						_A		:     Union[str, Any]				):
                      return model.decode(
                          decoder_input_ids=_A				,						decoder_attention_mask=_A				,						encoder_outputs=_A				,						)

                 with self.subTest('''JIT Enabled'''				):
                      lowercase :       List[Any]       =			decode_jitted(**_A				).to_tuple()

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






       """simple docstring"""







       for model_class_name in self.all_model_classes:
            lowercase :       Dict       =			model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''				)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            lowercase :       Dict       =			np.ones((1, 1)				) * model.config.eos_token_id
            lowercase :       str       =			model(_A				)
            self.assertIsNotNone(_A				)





import os
import sys
import unittest


lowerCAmelCase_						=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase_						=			os.path.join(git_repo_path, 'src', 'diffusers')
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			find_backend('''    if not is_torch_available():'''				)
       self.assertEqual(_A				,						'''torch'''				)

       # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
       # self.assertEqual(backend_with_underscore, "tensorflow_text")

       lowercase :       str       =			find_backend('''    if not (is_torch_available() and is_transformers_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers'''				)

       # double_backend_with_underscore = find_backend(
       #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
       # )
       # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

       lowercase :       str       =			find_backend(
           '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers_and_onnx'''				)






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






       """simple docstring"""







       lowercase :       Dict       =			read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('''torch'''				,						_A				)
       self.assertIn('''torch_and_transformers'''				,						_A				)
       self.assertIn('''flax_and_transformers'''				,						_A				)
       self.assertIn('''torch_and_transformers_and_onnx'''				,						_A				)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('''UNet2DModel'''				,						objects['''torch''']				)
       self.assertIn('''FlaxUNet2DConditionModel'''				,						objects['''flax''']				)
       self.assertIn('''StableDiffusionPipeline'''				,						objects['''torch_and_transformers''']				)
       self.assertIn('''FlaxStableDiffusionPipeline'''				,						objects['''flax_and_transformers''']				)
       self.assertIn('''LMSDiscreteScheduler'''				,						objects['''torch_and_scipy''']				)
       self.assertIn('''OnnxStableDiffusionPipeline'''				,						objects['''torch_and_transformers_and_onnx''']				)






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






       """simple docstring"""







       lowercase :       List[str]       =			create_dummy_object('''CONSTANT'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						'''\nCONSTANT = None\n'''				)

       lowercase :       List[Any]       =			create_dummy_object('''function'''				,						'''\'torch\''''				)
       self.assertEqual(
           _A				,						'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

       lowercase :       Tuple       =			'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
       lowercase :       List[Any]       =			create_dummy_object('''FakeClass'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						_A				)






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






       """simple docstring"""







       lowercase :       Optional[int]       =			'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
       lowercase :       int       =			create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
       self.assertEqual(dummy_files['''torch''']				,						_A				)