'''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 lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Union[str, Any], UpperCamelCase__      :			int, )  ->			Tuple:
        _A    		=  parent
        _A    		=  13
        _A    		=  7
        _A    		=  30
        _A    		=  self.seq_length + self.mem_len
        _A    		=  15
        _A    		=  True
        _A    		=  True
        _A    		=  99
        _A    		=  [10, 50, 80]
        _A    		=  32
        _A    		=  32
        _A    		=  4
        _A    		=  8
        _A    		=  1_28
        _A    		=  2
        _A    		=  2
        _A    		=  None
        _A    		=  1
        _A    		=  0
        _A    		=  3
        _A    		=  self.vocab_size - 1
        _A    		=  0.01
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )
        _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

        _A    		=  None
        if self.use_labels:
             _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

        _A    		=  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 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Tuple:
        random.seed(self.seed     )
        tf.random.set_seed(self.seed     )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			str, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int     )  ->			Dict:
        _A    		=  TFTransfoXLModel(_lowerCamelCase     )

        _A    		=  model(_lowerCamelCase     ).to_tuple()

        _A    		=  {'input_ids': input_ids_a, 'mems': mems_a}

        _A    		=  model(_lowerCamelCase     ).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 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			List[str], UpperCamelCase__      :			List[str], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Optional[Any]     )  ->			str:
        _A    		=  TFTransfoXLLMHeadModel(_lowerCamelCase     )

        _A    		=  model(_lowerCamelCase     ).to_tuple()

        _A    		=  {'input_ids': input_ids_a, 'labels': lm_labels}
        _A    		=  model(_lowerCamelCase     ).to_tuple()

        _A    		=  model([input_ids_a, mems_a]     ).to_tuple()

        _A    		=  {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

        _A    		=  model(_lowerCamelCase     ).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 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			int, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			List[str]     )  ->			Optional[Any]:
        _A    		=  TFTransfoXLForSequenceClassification(_lowerCamelCase     )
        _A    		=  model(_lowerCamelCase     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)     )

   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  self.prepare_config_and_inputs()
        (_A)    		=  config_and_inputs
        _A    		=  {'input_ids': input_ids_a}
        return config, inputs_dict







@require_tf
class lowercase_       (		A__    ,    A__    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   __lowerCAmelCase         =   () if is_tf_available() else ()
   __lowerCAmelCase         =   (
       {
           "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
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			int     )  ->			Union[str, Any]:



        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 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  TFTransfoXLModelTester(self     )
        _A    		=  ConfigTester(self, config_class=_lowerCamelCase, d_embed=37     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        self.model_tester.set_seed()
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        self.model_tester.set_seed()
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        _A    		=  [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             _A    		=  model_class(_lowerCamelCase     )
             assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer     )
             if model_class in list_other_models_with_output_ebd:
                  _A    		=  model.get_output_embeddings()
                  assert isinstance(_lowerCamelCase, tf.keras.layers.Layer     )
                  _A    		=  model.get_bias()
                  assert name is None
             else:
                  _A    		=  model.get_output_embeddings()
                  assert x is None
                  _A    		=  model.get_bias()
                  assert name is None
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        pass
   @slow
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:



        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    		=  TFTransfoXLModel.from_pretrained(_lowerCamelCase     )
             self.assertIsNotNone(_lowerCamelCase     )

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







@require_tf
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @unittest.skip('Skip test until #12651 is resolved.'     )
   @slow
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Any:
        _A    		=  TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'     )
        # fmt: off
        _A    		=  tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]], 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    		=  [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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    		=  model.generate(_lowerCamelCase, max_length=2_00, do_sample=_lowerCamelCase     )
        self.assertListEqual(output_ids[0].numpy().tolist(), _lowerCamelCase     )





"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
