


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 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''gelu'''


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=False							,					A_=99							,					A_=32							,					A_=2							,					A_=4							,					A_=37							,					A_=0.1							,					A_=0.1							,					A_=20							,					A_=2							,					A_=1							,					A_=0							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

       _UpperCAmelCase		:							Union[str, Any]							=     hidden_dropout_prob
       _UpperCAmelCase		:							str							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[Any]							=     eos_token_id
       _UpperCAmelCase		:							Optional[Any]							=     pad_token_id
       _UpperCAmelCase		:							Tuple							=     bos_token_id


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    )
       _UpperCAmelCase		:							Tuple							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,					1    )
       _UpperCAmelCase		:							List[Any]							=     tf.concat([input_ids, eos_tensor]							,					axis=1    )

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[Any]							=     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							,					)
       _UpperCAmelCase		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _UpperCAmelCase		:							List[Any]							=     ids_tensor((self.batch_size, 3)							,					config.vocab_size    )
       _UpperCAmelCase		:							List[str]							=     tf.cast(ids_tensor((self.batch_size, 3)							,					2    )							,					tf.inta    )

       # append to next input_ids and
       _UpperCAmelCase		:							Dict							=     tf.concat([input_ids, next_tokens]							,					axis=-1    )
       _UpperCAmelCase		:							Optional[Any]							=     tf.concat([attention_mask, next_attn_mask]							,					axis=-1    )

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     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
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

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




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							str							=     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:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     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 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     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?"
       )









import os
import string
import sys


SCREAMING_SNAKE_CASE__					:     List[str]       =     1 << 8

SCREAMING_SNAKE_CASE__					:     str       =     {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 27,
    'up': 65 + ARROW_KEY_FLAG,
    'down': 66 + ARROW_KEY_FLAG,
    'right': 67 + ARROW_KEY_FLAG,
    'left': 68 + ARROW_KEY_FLAG,
    'mod_int': 91,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 50,
    'delete': 51,
    'pg_up': 53,
    'pg_down': 54,
}

SCREAMING_SNAKE_CASE__					:     List[str]       =     KEYMAP['up']
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     KEYMAP['left']

if sys.platform == "win32":
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     []
 SCREAMING_SNAKE_CASE__					:     Dict       =     {
     b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
     b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
 }

for i in range(10):
 SCREAMING_SNAKE_CASE__					:     List[str]       =     ord(str(i))




def    a__    (		):
  if os.name == "nt":
    import msvcrt

    _UpperCAmelCase		:							int							=     """mbcs"""
    # Flush the keyboard buffer
    while msvcrt.kbhit():
      msvcrt.getch()
    if len(snake_case__     ) == 0:
      # Read the keystroke
      _UpperCAmelCase		:							Any							=     msvcrt.getch()

      # If it is a prefix char, get second part
      if ch in (b"\x00", b"\xe0"):
        _UpperCAmelCase		:							str							=     ch + msvcrt.getch()
        # Translate actual Win chars to bullet char types
        try:
          _UpperCAmelCase		:							Optional[int]							=     chr(WIN_KEYMAP[cha]     )
          WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]     )     )
          WIN_CH_BUFFER.append(snake_case__     )
          if ord(snake_case__     ) in (
              KEYMAP["insert"] - 1 << 9,
              KEYMAP["delete"] - 1 << 9,
              KEYMAP["pg_up"] - 1 << 9,
              KEYMAP["pg_down"] - 1 << 9,
          ):
            WIN_CH_BUFFER.append(chr(126     )     )
          _UpperCAmelCase		:							Any							=     chr(KEYMAP["""esc"""]     )
        except KeyError:
          _UpperCAmelCase		:							Optional[int]							=     cha[1]
      else:
        _UpperCAmelCase		:							Union[str, Any]							=     ch.decode(snake_case__     )
    else:
      _UpperCAmelCase		:							Optional[int]							=     WIN_CH_BUFFER.pop(0     )
  elif os.name == "posix":
    import termios
    import tty

    _UpperCAmelCase		:							List[str]							=     sys.stdin.fileno()
    _UpperCAmelCase		:							Optional[Any]							=     termios.tcgetattr(snake_case__     )
    try:
      tty.setraw(snake_case__     )
      _UpperCAmelCase		:							Tuple							=     sys.stdin.read(1     )
    finally:
      termios.tcsetattr(snake_case__						,   termios.TCSADRAIN						,   snake_case__     )
  return ch





def    a__    (		):
  _UpperCAmelCase		:							int							=     get_raw_chars()
  if ord(snake_case__     ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
    return char

  elif ord(snake_case__     ) == KEYMAP["esc"]:
    _UpperCAmelCase		:							int							=     get_raw_chars()
    if ord(snake_case__     ) == KEYMAP["mod_int"]:
      _UpperCAmelCase		:							Optional[Any]							=     get_raw_chars()
      if ord(snake_case__     ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__     ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
        return chr(ord(snake_case__     ) + ARROW_KEY_FLAG     )
      else:
        return KEYMAP["undefined"]
    else:
      return get_raw_chars()

  else:
    if char in string.printable:
      return char
    else:
      return KEYMAP["undefined"]





