


'''simple docstring'''

import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.generation import (
         FlaxForcedBOSTokenLogitsProcessor,
         FlaxForcedEOSTokenLogitsProcessor,
         FlaxLogitsProcessorList,
         FlaxMinLengthLogitsProcessor,
         FlaxTemperatureLogitsWarper,
         FlaxTopKLogitsWarper,
         FlaxTopPLogitsWarper,
     )





@require_flax
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  jnp.ones((batch_size, length)     ) / length
   return scores






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  None
   lowerCamelCase_      =  20

   lowerCamelCase_      =  self._get_uniform_logits(batch_size=2			,  length=UpperCamelCase     )

   # tweak scores to not be uniform anymore
   lowerCamelCase_      =  scores.at[1, 5].set((1 / length) + 0.1     )  # peak, 1st batch
   lowerCamelCase_      =  scores.at[1, 10].set((1 / length) - 0.4     )  # valley, 1st batch

   # compute softmax
   lowerCamelCase_      =  jax.nn.softmax(UpperCamelCase			,  axis=-1     )

   lowerCamelCase_      =  FlaxTemperatureLogitsWarper(temperature=0.5     )
   lowerCamelCase_      =  FlaxTemperatureLogitsWarper(temperature=1.3     )

   lowerCamelCase_      =  jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase			,  scores.copy()			,  cur_len=UpperCamelCase     )			,  axis=-1     )
   lowerCamelCase_      =  jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase			,  scores.copy()			,  cur_len=UpperCamelCase     )			,  axis=-1     )

   # uniform distribution stays uniform
   self.assertTrue(jnp.allclose(probs[0, :]			,  warped_prob_sharp[0, :]			,  atol=1e-3     )     )
   self.assertTrue(jnp.allclose(probs[0, :]			,  warped_prob_smooth[0, :]			,  atol=1e-3     )     )

   # sharp peaks get higher, valleys get lower
   self.assertLess(probs[1, :].max()			,  warped_prob_sharp[1, :].max()     )
   self.assertGreater(probs[1, :].min()			,  warped_prob_sharp[1, :].min()     )

   # smooth peaks get lower, valleys get higher
   self.assertGreater(probs[1, :].max()			,  warped_prob_smooth[1, :].max()     )
   self.assertLess(probs[1, :].min()			,  warped_prob_smooth[1, :].min()     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  None
   lowerCamelCase_      =  10
   lowerCamelCase_      =  2

   # create ramp distribution
   lowerCamelCase_      =  np.broadcast_to(np.arange(UpperCamelCase     )[None, :]			,  (batch_size, vocab_size)     ).copy()
   lowerCamelCase_      =  ramp_logits[1:, : vocab_size // 2] + vocab_size

   lowerCamelCase_      =  FlaxTopKLogitsWarper(3     )

   lowerCamelCase_      =  top_k_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )

   # check that correct tokens are filtered
   self.assertListEqual(jnp.isinf(scores[0]     ).tolist()			,  7 * [True] + 3 * [False]     )
   self.assertListEqual(jnp.isinf(scores[1]     ).tolist()			,  2 * [True] + 3 * [False] + 5 * [True]     )

   # check special case
   lowerCamelCase_      =  5
   lowerCamelCase_      =  FlaxTopKLogitsWarper(top_k=1			,  filter_value=0.0			,  min_tokens_to_keep=3     )

   lowerCamelCase_      =  np.broadcast_to(np.arange(UpperCamelCase     )[None, :]			,  (batch_size, length)     ).copy()
   lowerCamelCase_      =  top_k_warp_safety_check(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )

   # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
   self.assertListEqual((scores == 0.0).sum(axis=-1     ).tolist()			,  [2, 2]     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  None
   lowerCamelCase_      =  10
   lowerCamelCase_      =  2

   # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
   lowerCamelCase_      =  np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]     )     )

   lowerCamelCase_      =  FlaxTopPLogitsWarper(0.8     )
   lowerCamelCase_      =  np.exp(top_p_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )     )

   # dist should be filtered to keep min num values so that sum is >= top_p
   # exp (-inf) => 0
   lowerCamelCase_      =  np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]     )
   self.assertTrue(np.allclose(UpperCamelCase			,  UpperCamelCase			,  atol=1e-3     )     )

   # check edge cases with negative and extreme logits
   lowerCamelCase_      =  np.broadcast_to(np.arange(UpperCamelCase     )[None, :]			,  (batch_size, vocab_size)     ).copy() - (
       vocab_size // 2
   )

   # make ramp_logits more extreme
   lowerCamelCase_      =  ramp_logits[1] * 100.0

   # make sure at least 2 tokens are kept
   lowerCamelCase_      =  FlaxTopPLogitsWarper(0.9			,  min_tokens_to_keep=2			,  filter_value=0.0     )
   lowerCamelCase_      =  top_p_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )

   # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
   self.assertListEqual((filtered_dist != 0.0).sum(axis=-1     ).tolist()			,  [3, 2]     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  20
   lowerCamelCase_      =  4
   lowerCamelCase_      =  0

   lowerCamelCase_      =  FlaxMinLengthLogitsProcessor(min_length=10			,  eos_token_id=UpperCamelCase     )

   # check that min length is applied at length 5
   lowerCamelCase_      =  ids_tensor((batch_size, 20)			,  vocab_size=20     )
   lowerCamelCase_      =  5
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  min_dist_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()			,  4 * [-float("inf"     )]     )

   # check that min length is not applied anymore at length 15
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  15
   lowerCamelCase_      =  min_dist_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertFalse(jnp.isinf(UpperCamelCase     ).any()     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  20
   lowerCamelCase_      =  4
   lowerCamelCase_      =  0

   lowerCamelCase_      =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase     )

   # check that all scores are -inf except the bos_token_id score
   lowerCamelCase_      =  ids_tensor((batch_size, 1)			,  vocab_size=20     )
   lowerCamelCase_      =  1
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  logits_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]     ).all()     )
   self.assertListEqual(scores[:, bos_token_id].tolist()			,  4 * [0]     )  # score for bos_token_id shold be zero

   # check that bos_token_id is not forced if current length is greater than 1
   lowerCamelCase_      =  3
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  logits_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertFalse(jnp.isinf(UpperCamelCase     ).any()     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  20
   lowerCamelCase_      =  4
   lowerCamelCase_      =  0
   lowerCamelCase_      =  5

   lowerCamelCase_      =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase			,  eos_token_id=UpperCamelCase     )

   # check that all scores are -inf except the eos_token_id when max_length is reached
   lowerCamelCase_      =  ids_tensor((batch_size, 4)			,  vocab_size=20     )
   lowerCamelCase_      =  4
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  logits_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]     ).all()     )
   self.assertListEqual(scores[:, eos_token_id].tolist()			,  4 * [0]     )  # score for eos_token_id should be zero

   # check that eos_token_id is not forced if max_length is not reached
   lowerCamelCase_      =  3
   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  logits_processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   self.assertFalse(jnp.isinf(UpperCamelCase     ).any()     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  4
   lowerCamelCase_      =  10
   lowerCamelCase_      =  15
   lowerCamelCase_      =  2
   lowerCamelCase_      =  1
   lowerCamelCase_      =  15

   # dummy input_ids and scores
   lowerCamelCase_      =  ids_tensor((batch_size, sequence_length)			,  UpperCamelCase     )
   lowerCamelCase_      =  input_ids.copy()

   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  scores.copy()

   # instantiate all dist processors
   lowerCamelCase_      =  FlaxTemperatureLogitsWarper(temperature=0.5     )
   lowerCamelCase_      =  FlaxTopKLogitsWarper(3     )
   lowerCamelCase_      =  FlaxTopPLogitsWarper(0.8     )

   # instantiate all logits processors
   lowerCamelCase_      =  FlaxMinLengthLogitsProcessor(min_length=10			,  eos_token_id=UpperCamelCase     )
   lowerCamelCase_      =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase     )
   lowerCamelCase_      =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase			,  eos_token_id=UpperCamelCase     )

   lowerCamelCase_      =  10

   # no processor list
   lowerCamelCase_      =  temp_dist_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   lowerCamelCase_      =  top_k_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   lowerCamelCase_      =  top_p_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   lowerCamelCase_      =  min_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   lowerCamelCase_      =  bos_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
   lowerCamelCase_      =  eos_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )

   # with processor list
   lowerCamelCase_      =  FlaxLogitsProcessorList(
       [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
   lowerCamelCase_      =  processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )

   # scores should be equal
   self.assertTrue(jnp.allclose(UpperCamelCase			,  UpperCamelCase			,  atol=1e-3     )     )

   # input_ids should never be changed
   self.assertListEqual(input_ids.tolist()			,  input_ids_comp.tolist()     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  4
   lowerCamelCase_      =  10
   lowerCamelCase_      =  15
   lowerCamelCase_      =  2
   lowerCamelCase_      =  1
   lowerCamelCase_      =  15

   # dummy input_ids and scores
   lowerCamelCase_      =  ids_tensor((batch_size, sequence_length)			,  UpperCamelCase     )
   lowerCamelCase_      =  input_ids.copy()

   lowerCamelCase_      =  self._get_uniform_logits(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  scores.copy()

   # instantiate all dist processors
   lowerCamelCase_      =  FlaxTemperatureLogitsWarper(temperature=0.5     )
   lowerCamelCase_      =  FlaxTopKLogitsWarper(3     )
   lowerCamelCase_      =  FlaxTopPLogitsWarper(0.8     )

   # instantiate all logits processors
   lowerCamelCase_      =  FlaxMinLengthLogitsProcessor(min_length=10			,  eos_token_id=UpperCamelCase     )
   lowerCamelCase_      =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase     )
   lowerCamelCase_      =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase			,  eos_token_id=UpperCamelCase     )

   lowerCamelCase_      =  10

   # no processor list
   def run_no_processor_list(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  temp_dist_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    lowerCamelCase_      =  top_k_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    lowerCamelCase_      =  top_p_warp(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    lowerCamelCase_      =  min_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    lowerCamelCase_      =  bos_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    lowerCamelCase_      =  eos_dist_proc(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    return scores

   # with processor list
   def run_processor_list(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  FlaxLogitsProcessorList(
        [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
    lowerCamelCase_      =  processor(UpperCamelCase			,  UpperCamelCase			,  cur_len=UpperCamelCase     )
    return scores

   lowerCamelCase_      =  jax.jit(UpperCamelCase     )
   lowerCamelCase_      =  jax.jit(UpperCamelCase     )

   lowerCamelCase_      =  jitted_run_no_processor_list(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  jitted_run_processor_list(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   # scores should be equal
   self.assertTrue(jnp.allclose(UpperCamelCase			,  UpperCamelCase			,  atol=1e-3     )     )

   # input_ids should never be changed
   self.assertListEqual(input_ids.tolist()			,  input_ids_comp.tolist()     )









from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						BlenderbotConfig
 UpperCamelCase						   =						{}
 UpperCamelCase						   =						'''gelu'''

 def __init__(				self					:  int						,							A_					:  Optional[int]						,							A_					:  List[str]=13						,							A_					:  str=7						,							A_					:  Any=True						,							A_					:  Any=False						,							A_					:  Optional[Any]=99						,							A_					:  List[str]=32						,							A_					:  List[str]=2						,							A_					:  Dict=4						,							A_					:  List[str]=37						,							A_					:  List[str]=0.1						,							A_					:  Optional[int]=0.1						,							A_					:  str=20						,							A_					:  str=2						,							A_					:  Optional[Any]=1						,							A_					:  int=0						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    seq_length
       lowerCamelCase_		     =    is_training
       lowerCamelCase_		     =    use_labels
       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    intermediate_size

       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    max_position_embeddings
       lowerCamelCase_		     =    eos_token_id
       lowerCamelCase_		     =    pad_token_id
       lowerCamelCase_		     =    bos_token_id

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size  )
       lowerCamelCase_		     =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )						,							1  )
       lowerCamelCase_		     =    tf.concat([input_ids, eos_tensor]						,							axis=1  )

       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )

       lowerCamelCase_		     =    self.config_cls(
           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_ids=[2]						,							bos_token_id=self.bos_token_id						,							pad_token_id=self.pad_token_id						,							decoder_start_token_id=self.pad_token_id						,							**self.config_updates						,							)
       lowerCamelCase_		     =    prepare_blenderbot_inputs_dict(A_						,							A_						,							A_  )
       return config, inputs_dict





 def 				a__ (				self					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  List[str]  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModel(config=A_  ).get_decoder()
       lowerCamelCase_		     =    inputs_dict['input_ids']

       lowerCamelCase_		     =    input_ids[:1, :]
       lowerCamelCase_		     =    inputs_dict['attention_mask'][:1, :]
       lowerCamelCase_		     =    inputs_dict['head_mask']
       lowerCamelCase_		     =    1

       # first forward pass
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_						,							head_mask=A_						,							use_cache=A_  )

       lowerCamelCase_     ,				lowerCamelCase_		     =    outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowerCamelCase_		     =    ids_tensor((self.batch_size, 3)						,							config.vocab_size  )
       lowerCamelCase_		     =    tf.cast(ids_tensor((self.batch_size, 3)						,							2  )						,							tf.inta  )

       # append to next input_ids and
       lowerCamelCase_		     =    tf.concat([input_ids, next_tokens]						,							axis=-1  )
       lowerCamelCase_		     =    tf.concat([attention_mask, next_attn_mask]						,							axis=-1  )

       lowerCamelCase_		     =    model(A_						,							attention_mask=A_  )[0]
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_						,							past_key_values=A_  )[0]

       self.parent.assertEqual(next_tokens.shape[1]						,							output_from_past.shape[1]  )

       # select random slice
       lowerCamelCase_		     =    int(ids_tensor((1,)						,							output_from_past.shape[-1]  )  )
       lowerCamelCase_		     =    output_from_no_past[:, -3:, random_slice_idx]
       lowerCamelCase_		     =    output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(A_						,							A_						,							rtol=1E-3  )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       Any	,							lowercase	:       Tuple	,							lowercase	:       List[Any]=None	,							lowercase	:       List[str]=None	,							lowercase	:       List[Any]=None	,							lowercase	:       Tuple=None	,							lowercase	:       Union[str, Any]=None	,							):


      '''simple docstring'''



      if attention_mask is None:
            lowerCamelCase_		     =    tf.cast(tf.math.not_equal(lowercase	,							config.pad_token_id     )	,							tf.inta     )
      if decoder_attention_mask is None:
            lowerCamelCase_		     =    tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape	,							dtype=tf.inta     ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,							config.pad_token_id     )	,							tf.inta     ),
                ]	,							axis=-1	,							)
      if head_mask is None:
            lowerCamelCase_		     =    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            lowerCamelCase_		     =    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            lowerCamelCase_		     =    tf.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": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }







@require_tf
class   A(						UpperCamelCase							,      UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 UpperCamelCase						   =						(
     {
         '''conversational''': TFBlenderbotForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotModel,
         '''summarization''': TFBlenderbotForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotForConditionalGeneration,
         '''translation''': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						   =						True
 UpperCamelCase						   =						False
 UpperCamelCase						   =						False

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

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModelTester(self  )
       lowerCamelCase_		     =    ConfigTester(self						,							config_class=A_  )

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

       """simple docstring"""






       self.config_tester.run_common_tests()





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

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_  )







@require_tokenizers
@require_tf
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''My friends are cool but they eat too many carbs.''']
 UpperCamelCase						   =						'''facebook/blenderbot-400M-distill'''

 @cached_property
 def 				a__ (				self					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       return BlenderbotTokenizer.from_pretrained(self.model_name  )

 @cached_property
 def 				a__ (				self					:  List[Any]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
       return model





 @slow
 def 				a__ (				self					:  str  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    self.tokenizer(self.src_text						,							return_tensors='tf'  )
       lowerCamelCase_		     =    self.model.generate(
           model_inputs.input_ids						,							)
       lowerCamelCase_		     =    self.tokenizer.batch_decode(generated_ids.numpy()						,							skip_special_tokens=A_  )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )



