
'''simple docstring'''



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


_lowercase          =						False
try:
						_lowercase          =						_is_package_available("""google.colab""")
except ModuleNotFoundError:
						pass




@input.register
class     UpperCAmelCase_	:






						'''simple docstring'''






						def __init__(   self       ,  _lowercase = None       ,  _lowercase = [] ):

												"""simple docstring"""



												_lowerCAmelCase    		=					0
												_lowerCAmelCase    		=					choices
												_lowerCAmelCase    		=					prompt
												if sys.platform == "win32":
																		_lowerCAmelCase    		=					"""*"""
												else:
																		_lowerCAmelCase    		=					"""➔ """




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = "" ):

												"""simple docstring"""



												if sys.platform != "win32":
																		writeColor(self.choices[index]       ,  32       ,  _lowercase )
												else:
																		forceWrite(self.choices[index]       ,  _lowercase )




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												if index == self.position:
																		forceWrite(F' {self.arrow_char} ' )
																		self.write_choice(_lowercase )
												else:
																		forceWrite(F'    {self.choices[index]}' )
												reset_cursor()




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = 1 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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(_lowercase )
												move_cursor(_lowercase       ,  direction.name )
												self.print_choice(self.position )




						@input.mark(KEYMAP["""up"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.UP )




						@input.mark(KEYMAP["""down"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.DOWN )




						@input.mark(KEYMAP["""newline"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@input.mark(KEYMAP["""interrupt"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					int(chr(self.current_selection ) )
												_lowerCAmelCase    		=					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       ,  _lowercase )
																		else:
																								return
												else:
																		return




						def    _lowercase      (   self       ,  _lowercase = 0 ):

												"""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""" )
												_lowerCAmelCase    		=					default_choice
												for i in range(len(self.choices ) ):
																		self.print_choice(_lowercase )
																		forceWrite("""\n""" )
												move_cursor(len(self.choices ) - self.position       ,  """UP""" )
												with cursor.hide():
																		while True:
																								if in_colab:
																														try:
																																				_lowerCAmelCase    		=					int(builtins.input() )
																														except ValueError:
																																				_lowerCAmelCase    		=					default_choice
																								else:
																														_lowerCAmelCase    		=					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(_lowercase       ,  """\n""" )
																														return choice


'''simple docstring'''




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 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     )					->							int:






         """simple docstring"""






         A			:  str   =		parent
         A			:  Optional[int]   =		13
         A			:  Union[str, Any]   =		7
         A			:  Optional[Any]   =		30
         A			:  Optional[int]   =		self.seq_length + self.mem_len
         A			:  int   =		15
         A			:  Any   =		True
         A			:  Union[str, Any]   =		True
         A			:  Optional[int]   =		99
         A			:  Any   =		[10, 50, 80]
         A			:  Any   =		32
         A			:  Any   =		32
         A			:  Any   =		4
         A			:  Any   =		8
         A			:  Optional[int]   =		128
         A			:  Union[str, Any]   =		2
         A			:  List[str]   =		2
         A			:  Optional[Any]   =		None
         A			:  int   =		1
         A			:  str   =		0
         A			:  Optional[int]   =		3
         A			:  Union[str, Any]   =		self.vocab_size - 1
         A			:  str   =		0.01





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )
         A			:  str   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

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

         A			:  Optional[Any]   =		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 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		TFTransfoXLModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  int   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

         A,    A			:  Any   =		model(SCREAMING_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 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Tuple   =		TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Tuple   =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         A,    A			:  Dict   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A,    A			:  Optional[Any]   =		model([input_ids_a, mems_a]      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

         A,    A			:  List[Any]   =		model(SCREAMING_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 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  List[Any]   =		TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)      )





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






         """simple docstring"""






         A			:  List[Any]   =		self.prepare_config_and_inputs()
         ((A),    (A),    (A),    (A))			:  Dict   =		config_and_inputs
         A			:  List[str]   =		{'''input_ids''': input_ids_a}
         return config, inputs_dict





@require_tf
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     __magic_name__          =						() if is_tf_available() else ()
     __magic_name__          =						(
         {
             '''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
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  str   =		TFTransfoXLModelTester(self      )
         A			:  Optional[int]   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     d_embed=37      )





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






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Tuple   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  int   =		[TFTransfoXLForSequenceClassification]

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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         pass





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  Union[str, Any]   =		TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





     @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





@require_tf
class 					A							(				unittest.TestCase		):





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






         """simple docstring"""






         A			:  List[str]   =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
         # fmt: off
         A			:  List[str]   =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]		,     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
         A			:  str   =		[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

         A			:  Optional[Any]   =		model.generate(SCREAMING_SNAKE_CASE		,     max_length=200		,     do_sample=SCREAMING_SNAKE_CASE      )
         self.assertListEqual(output_ids[0].numpy().tolist()		,     SCREAMING_SNAKE_CASE      )




