"""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
    UpperCAmelCase__     :   Optional[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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase=None   ,lowercase=None   ,lowercase=None   ,lowercase=None   ,lowercase=None   ,lowercase=None   ,):







  """simple docstring"""
  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        a				:


    def __init__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]=13   ,				__lowerCAmelCase	:  int=7   ,				__lowerCAmelCase	:  str=True   ,				__lowerCAmelCase	:  Union[str, Any]=False   ,				__lowerCAmelCase	:  Optional[Any]=99   ,				__lowerCAmelCase	:  str=16   ,				__lowerCAmelCase	:  str=2   ,				__lowerCAmelCase	:  Tuple=4   ,				__lowerCAmelCase	:  str=4   ,				__lowerCAmelCase	:  Optional[Any]="gelu"   ,				__lowerCAmelCase	:  List[str]=0.1   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  List[Any]=32   ,				__lowerCAmelCase	:  str=2   ,				__lowerCAmelCase	:  str=1   ,				__lowerCAmelCase	:  List[str]=0   ,				__lowerCAmelCase	:  Optional[Any]=0.02   ,				):
      _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      lowerCAmelCase_  (					self	:  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(lowerCamelCase__   ,				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=lowerCamelCase__   ,				)
      _UpperCAmelCase						 =      prepare_blenderbot_inputs_dict(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__					)
      return config, inputs_dict


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      return config, inputs_dict


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      20
      _UpperCAmelCase						 =      model_class_name(lowerCamelCase__					)

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

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

      _UpperCAmelCase						 =      model.init_cache(decoder_input_ids.shape[0]   ,				lowerCamelCase__   ,				lowerCamelCase__					)
      _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]   ,				lowerCamelCase__   ,				decoder_attention_mask=lowerCamelCase__   ,				past_key_values=lowerCamelCase__   ,				decoder_position_ids=lowerCamelCase__   ,				)

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

      _UpperCAmelCase						 =      model.decode(lowerCamelCase__   ,				lowerCamelCase__					)

      _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      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      20
      _UpperCAmelCase						 =      model_class_name(lowerCamelCase__					)

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

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

      _UpperCAmelCase						 =      model.decode(lowerCamelCase__   ,				lowerCamelCase__   ,				decoder_attention_mask=lowerCamelCase__					)

      _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        a				(		unittest.TestCase   ):
    _snake_case     :  Tuple     							=							99


    def      lowerCAmelCase_  (					self	:  Dict					):
      _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      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self._get_config_and_data()
      _UpperCAmelCase						 =      FlaxBlenderbotForConditionalGeneration(lowerCamelCase__					)
      _UpperCAmelCase						 =      lm_model(input_ids=lowerCamelCase__					)
      _UpperCAmelCase						 =      (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape   ,				lowerCamelCase__					)


    def      lowerCAmelCase_  (					self	:  Union[str, 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(lowerCamelCase__					)
      _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=lowerCamelCase__   ,				decoder_input_ids=lowerCamelCase__					)
      _UpperCAmelCase						 =      (*summary.shape, config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape   ,				lowerCamelCase__					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,				dtype=np.intaa					)
      _UpperCAmelCase						 =      shift_tokens_right(lowerCamelCase__   ,				1   ,				2					)
      _UpperCAmelCase						 =      np.equal(lowerCamelCase__   ,				1					).astype(np.floataa					).sum()
      _UpperCAmelCase						 =      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				(		UpperCAmelCase__	,							unittest.TestCase	,							UpperCAmelCase__   ):
    _snake_case     :  int     							=							True
    _snake_case     :  Tuple     							=							(
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    _snake_case     :  List[str]     							=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      FlaxBlenderbotModelTester(self					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  str					):
      _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(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _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(lowerCamelCase__   ,				lowerCamelCase__					)
          _UpperCAmelCase						 =      model_class(lowerCamelCase__					)

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

          with self.subTest("""JIT Enabled"""					):
            _UpperCAmelCase						 =      encode_jitted(**lowerCamelCase__					).to_tuple()

          with self.subTest("""JIT Disabled"""					):
            with jax.disable_jit():
              _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[str]					):
      _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(lowerCamelCase__					)
          _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(__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  int					):
            return model.decode(
                decoder_input_ids=lowerCamelCase__   ,				decoder_attention_mask=lowerCamelCase__   ,				encoder_outputs=lowerCamelCase__   ,				)

          with self.subTest("""JIT Enabled"""					):
            _UpperCAmelCase						 =      decode_jitted(**lowerCamelCase__					).to_tuple()

          with self.subTest("""JIT Disabled"""					):
            with jax.disable_jit():
              _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[str]					):







      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(lowerCamelCase__					)
        self.assertIsNotNone(lowerCamelCase__					)


    @unittest.skipUnless(jax_device != """cpu"""   ,				"""3B test too slow on CPU."""					)
    @slow
    def      lowerCAmelCase_  (					self	:  str					):
      _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=lowerCamelCase__					)
      _UpperCAmelCase						 =      BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""					)

      _UpperCAmelCase						 =      ["Sam"]
      _UpperCAmelCase						 =      tokenizer(lowerCamelCase__   ,				return_tensors="""jax"""					)

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

      _UpperCAmelCase						 =      tokenizer.batch_decode(lowerCamelCase__   ,				**lowerCamelCase__					)
      assert generated_txt[0].strip() == tgt_text

"""simple docstring"""


# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  		=						re.compile(r"""([A-Z]+)([A-Z][a-z])""")
UpperCAmelCase__  		=						re.compile(r"""([a-z\d])([A-Z])""")

UpperCAmelCase__  		=						re.compile(r"""(?<!_)_(?!_)""")
UpperCAmelCase__  		=						re.compile(r"""(_{2,})""")

UpperCAmelCase__  		=						r"""^\w+(\.\w+)*$"""

UpperCAmelCase__  		=						r"""<>:/\|?*"""






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _uppercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  _UpperCAmelCase						 =      _lowercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  return name.lower()






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _single_underscore_re.split(lowercase		)
  _UpperCAmelCase						 =      [_multiple_underscores_re.split(lowercase		) for n in name]
  return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase		) if n != """"""		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  return camelcase_to_snakecase(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  if not re.match(_split_re   ,lowercase		):
    raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''		)
  return f'''{filename_prefix_for_name(lowercase		)}-{split}'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  if filetype_suffix:
    prefix += f'''.{filetype_suffix}'''
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
  return f'''{filepath}*'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)

  if shard_lengths:
    _UpperCAmelCase						 =      len(lowercase		)
    _UpperCAmelCase						 =      [f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase		)]
    if filetype_suffix:
      _UpperCAmelCase						 =      [filename + f'''.{filetype_suffix}''' for filename in filenames]
    return filenames
  else:
    _UpperCAmelCase						 =      prefix
    if filetype_suffix:
      filename += f'''.{filetype_suffix}'''
    return [filename]
