from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )





class 		_lowercase						:

 def __init__( self	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      )   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					parent
  UpperCamelCase_					:						str =					1_3
  UpperCamelCase_					:						List[Any] =					7
  UpperCamelCase_					:						Union[str, Any] =					3_0
  UpperCamelCase_					:						Union[str, Any] =					self.seq_length + self.mem_len
  UpperCamelCase_					:						Union[str, Any] =					1_5
  UpperCamelCase_					:						int =					True
  UpperCamelCase_					:						List[str] =					True
  UpperCamelCase_					:						str =					9_9
  UpperCamelCase_					:						Union[str, Any] =					[1_0, 5_0, 8_0]
  UpperCamelCase_					:						List[str] =					3_2
  UpperCamelCase_					:						Any =					3_2
  UpperCamelCase_					:						Optional[int] =					4
  UpperCamelCase_					:						Any =					8
  UpperCamelCase_					:						List[str] =					1_2_8
  UpperCamelCase_					:						Tuple =					2
  UpperCamelCase_					:						Any =					2
  UpperCamelCase_					:						Optional[Any] =					None
  UpperCamelCase_					:						Dict =					1
  UpperCamelCase_					:						Union[str, Any] =					0
  UpperCamelCase_					:						Optional[int] =					3
  UpperCamelCase_					:						Union[str, Any] =					self.vocab_size - 1
  UpperCamelCase_					:						int =					0.01

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)
  UpperCamelCase_					:						List[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Optional[int] =					None
  if self.use_labels:
   UpperCamelCase_					:						List[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Tuple =					TransfoXLConfig(
      vocab_size=self.vocab_size   ,      mem_len=self.mem_len   ,      clamp_len=self.clamp_len   ,      cutoffs=self.cutoffs   ,      d_model=self.hidden_size   ,      d_embed=self.d_embed   ,      n_head=self.num_attention_heads   ,      d_head=self.d_head   ,      d_inner=self.d_inner   ,      div_val=self.div_val   ,      n_layer=self.num_hidden_layers   ,      eos_token_id=self.eos_token_id   ,      pad_token_id=self.vocab_size - 1   ,      init_range=self.init_range   ,      num_labels=self.num_labels   ,      )

  return (config, input_ids_a, input_ids_a, lm_labels)

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

  """simple docstring"""





  random.seed(self.seed			)
  tf.random.set_seed(self.seed			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Any			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					TFTransfoXLModel(snake_case			)

  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					model(snake_case			).to_tuple()

  UpperCamelCase_					:						List[str] =					{'input_ids': input_ids_a, 'mems': mems_a}

  UpperCamelCase_, UpperCamelCase_					:						str =					model(snake_case			).to_tuple()

  self.parent.assertEqual(hidden_states_a.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)
  self.parent.assertEqual(hidden_states_a.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)
  self.parent.assertListEqual(
      [mem.shape for mem in mems_a]   ,      [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,      )
  self.parent.assertListEqual(
      [mem.shape for mem in mems_a]   ,      [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							int			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					TFTransfoXLLMHeadModel(snake_case			)

  UpperCamelCase_, UpperCamelCase_					:						List[str] =					model(snake_case			).to_tuple()

  UpperCamelCase_					:						int =					{'input_ids': input_ids_a, 'labels': lm_labels}
  UpperCamelCase_, UpperCamelCase_					:						Dict =					model(snake_case			).to_tuple()

  UpperCamelCase_, UpperCamelCase_					:						Tuple =					model([input_ids_a, mems_a]			).to_tuple()

  UpperCamelCase_					:						Optional[Any] =					{'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					model(snake_case			).to_tuple()

  self.parent.assertEqual(lm_logits_a.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)
  self.parent.assertListEqual(
      [mem.shape for mem in mems_a]   ,      [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,      )

  self.parent.assertEqual(lm_logits_a.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)
  self.parent.assertListEqual(
      [mem.shape for mem in mems_a]   ,      [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Tuple			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					TFTransfoXLForSequenceClassification(snake_case			)
  UpperCamelCase_					:						List[str] =					model(snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)			)


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

  """simple docstring"""





  UpperCamelCase_					:						int =					self.prepare_config_and_inputs()
  ((UpperCamelCase_), (UpperCamelCase_), (UpperCamelCase_), (UpperCamelCase_))					:						Union[str, Any] =					config_and_inputs
  UpperCamelCase_					:						Union[str, Any] =					{'input_ids': input_ids_a}
  return config, inputs_dict





@require_tf
class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   (
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 lowercase				     =   () if is_tf_available() else ()
 lowercase				     =   (
     {
         'feature-extraction': TFTransfoXLModel,
         'text-classification': TFTransfoXLForSequenceClassification,
         'text-generation': TFTransfoXLLMHeadModel,
         'zero-shot': TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 lowercase				     =   False
 lowercase				     =   False
 lowercase				     =   False
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							Any   ,      snake_case	:							str			)   ->						int:

  """simple docstring"""





  if pipeline_test_casse_name == "TextGenerationPipelineTests":
   # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
   # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
   # tokenizer.
   return True

  return False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					TFTransfoXLModelTester(self			)
  UpperCamelCase_					:						Optional[Any] =					ConfigTester(self   ,      config_class=snake_case   ,      d_embed=3_7			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





  self.config_tester.run_common_tests()

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

  """simple docstring"""





  self.model_tester.set_seed()
  UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_model(*snake_case			)

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

  """simple docstring"""





  self.model_tester.set_seed()
  UpperCamelCase_					:						Any =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[str] =					self.model_tester.prepare_config_and_inputs_for_common()
  UpperCamelCase_					:						Dict =					[TFTransfoXLForSequenceClassification]

  for model_class in self.all_model_classes:
   UpperCamelCase_					:						str =					model_class(snake_case			)
   assert isinstance(model.get_input_embeddings()   ,      tf.keras.layers.Layer			)
   if model_class in list_other_models_with_output_ebd:
    UpperCamelCase_					:						Any =					model.get_output_embeddings()
    assert isinstance(snake_case   ,      tf.keras.layers.Layer			)
    UpperCamelCase_					:						Any =					model.get_bias()
    assert name is None
   else:
    UpperCamelCase_					:						Optional[int] =					model.get_output_embeddings()
    assert x is None
    UpperCamelCase_					:						str =					model.get_bias()
    assert name is None

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

  """simple docstring"""





  pass

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						List[str]:

  """simple docstring"""





  for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCamelCase_					:						List[str] =					TFTransfoXLModel.from_pretrained(snake_case			)
   self.assertIsNotNone(snake_case			)


 @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						str:

  """simple docstring"""





  pass





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 @unittest.skip('Skip test until #12651 is resolved.'			)
 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'			)
  # fmt: off
  UpperCamelCase_					:						Tuple =					tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]   ,      dtype=tf.intaa			)  # noqa: E231
  # fmt: on
  #  In 1991 , the remains of Russian Tsar Nicholas II and his family
  #  ( except for Alexei and Maria ) are discovered .
  #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
  #  remainder of the story . 1883 Western Siberia ,
  #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
  #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
  #  father initially slaps him for making such an accusation , Rasputin watches as the
  #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
  #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
  #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

  # fmt: off
  UpperCamelCase_					:						Optional[Any] =					[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
  # fmt: on
  #  In 1991, the remains of Russian Tsar Nicholas II and his family (
  #  except for Alexei and Maria ) are discovered. The voice of young son,
  #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
  #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
  #  and a group of men to perform magic. Rasputin has a vision and
  #  denounces one of the men as a horse thief. Although his father initially
  #  slaps him for making such an accusation, Rasputin watches as the man
  #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
  #  of the Virgin Mary, prompting him to become a priest.
  #  Rasputin quickly becomes famous, with people, even a bishop, begging for
  #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
  # Nicholas II and his family were discovered. The voice of <unk> young son,
  # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

  UpperCamelCase_					:						List[Any] =					model.generate(snake_case   ,      max_length=2_0_0   ,      do_sample=snake_case			)
  self.assertListEqual(output_ids[0].numpy().tolist()   ,      snake_case			)




import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


a_    					=       False
try:
   a_    					=       _is_package_available('google.colab')
except ModuleNotFoundError:
   pass





@input.register
class 		_lowercase						:

 def __init__( self	:							Dict   ,      snake_case	:							str = None   ,      snake_case	:							list = []			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					0
  UpperCamelCase_					:						Optional[Any] =					choices
  UpperCamelCase_					:						Any =					prompt
  if sys.platform == "win32":
   UpperCamelCase_					:						Optional[Any] =					'*'
  else:
   UpperCamelCase_					:						str =					'➔ '

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							str = ""			)   ->						List[Any]:

  """simple docstring"""





  if sys.platform != "win32":
   writeColor(self.choices[index]   ,      3_2   ,      snake_case			)
  else:
   forceWrite(self.choices[index]   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							int			)   ->						List[Any]:

  """simple docstring"""





  if index == self.position:
   forceWrite(f" {self.arrow_char} "			)
   self.write_choice(snake_case			)
  else:
   forceWrite(f"    {self.choices[index]}"			)
  reset_cursor()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Direction   ,      snake_case	:							int = 1			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.position
  if direction == Direction.DOWN:
   if self.position + 1 >= len(self.choices			):
    return
   self.position += num_spaces
  else:
   if self.position - 1 < 0:
    return
   self.position -= num_spaces
  clear_line()
  self.print_choice(snake_case			)
  move_cursor(snake_case   ,      direction.name			)
  self.print_choice(self.position			)

 @input.mark(KEYMAP['up']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  self.move_direction(Direction.UP			)

 @input.mark(KEYMAP['down']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[Any]:

  """simple docstring"""





  self.move_direction(Direction.DOWN			)

 @input.mark(KEYMAP['newline']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  return self.position

 @input.mark(KEYMAP['interrupt']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  raise KeyboardInterrupt

 @input.mark_multiple(*[KEYMAP[str(snake_case			)] for number in range(1_0			)]			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					int(chr(self.current_selection			)			)
  UpperCamelCase_					:						Optional[int] =					index - self.position
  if index == self.position:
   return
  if index < len(self.choices			):
   if self.position > index:
    self.move_direction(Direction.UP   ,      -movement			)
   elif self.position < index:
    self.move_direction(Direction.DOWN   ,      snake_case			)
   else:
    return
  else:
   return


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							int = 0			)   ->						Union[str, Any]:

  """simple docstring"""





  if self.prompt:
   linebreak()
   forceWrite(self.prompt   ,      '\n'			)
   if in_colab:
    forceWrite('Please input a choice index (starting from 0), and press enter'   ,      '\n'			)
   else:
    forceWrite('Please select a choice using the arrow or number keys, and selecting with enter'   ,      '\n'			)
  UpperCamelCase_					:						Optional[int] =					default_choice
  for i in range(len(self.choices			)			):
   self.print_choice(snake_case			)
   forceWrite('\n'			)
  move_cursor(len(self.choices			) - self.position   ,      'UP'			)
  with cursor.hide():
   while True:
    if in_colab:
     try:
      UpperCamelCase_					:						Tuple =					int(builtins.input()			)
     except ValueError:
      UpperCamelCase_					:						Tuple =					default_choice
    else:
     UpperCamelCase_					:						Optional[Any] =					self.handle_input()
    if choice is not None:
     reset_cursor()
     for _ in range(len(self.choices			) + 1			):
      move_cursor(1   ,      'UP'			)
      clear_line()
     self.write_choice(snake_case   ,      '\n'			)
     return choice



