
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   ,A__   ,):
												lowercase							=						parent
												lowercase							=						1_3
												lowercase							=						7
												lowercase							=						3_0
												lowercase							=						self.seq_length + self.mem_len
												lowercase							=						1_5
												lowercase							=						True
												lowercase							=						True
												lowercase							=						9_9
												lowercase							=						[1_0, 5_0, 8_0]
												lowercase							=						3_2
												lowercase							=						3_2
												lowercase							=						4
												lowercase							=						8
												lowercase							=						1_2_8
												lowercase							=						2
												lowercase							=						2
												lowercase							=						None
												lowercase							=						1
												lowercase							=						0
												lowercase							=						3
												lowercase							=						self.vocab_size - 1
												lowercase							=						0.01




							def  A__      (				self):
												lowercase							=						ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size)
												lowercase							=						ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size)

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

												lowercase							=						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  A__      (				self):
												random.seed(self.seed)
												tf.random.set_seed(self.seed)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						TFTransfoXLModel(_lowerCamelCase)

												lowercase	,						lowercase							=						model(_lowerCamelCase).to_tuple()

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

												lowercase	,						lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						TFTransfoXLLMHeadModel(_lowerCamelCase)

												lowercase	,						lowercase							=						model(_lowerCamelCase).to_tuple()

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

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

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

												lowercase	,						lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						TFTransfoXLForSequenceClassification(_lowerCamelCase)
												lowercase							=						model(_lowerCamelCase)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels))





							def  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												((lowercase)	,						(lowercase)	,						(lowercase)	,						(lowercase))							=						config_and_inputs
												lowercase							=						{'''input_ids''': input_ids_a}
												return config, inputs_dict







@require_tf
class 				lowercase		(					_lowerCAmelCase       ,  _lowerCAmelCase       ,  unittest.TestCase		):
							lowercase_       :       str       					=(
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowercase_       :       Optional[int]       					=() if is_tf_available() else ()
							lowercase_       :       Optional[Any]       					=(
							    {
							        "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_       :       int       					=False
							lowercase_       :       Optional[int]       					=False
							lowercase_       :       Dict       					=False
							lowercase_       :       str       					=False




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__):
												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  A__      (				self):
												lowercase							=						TFTransfoXLModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=_lowerCamelCase   ,d_embed=3_7)




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




							def  A__      (				self):
												self.model_tester.set_seed()
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase)




							def  A__      (				self):
												self.model_tester.set_seed()
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase)




							def  A__      (				self):
												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()
												lowercase							=						[TFTransfoXLForSequenceClassification]

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(_lowerCamelCase)
																	assert isinstance(model.get_input_embeddings()   ,tf.keras.layers.Layer)
																	if model_class in list_other_models_with_output_ebd:
																						lowercase							=						model.get_output_embeddings()
																						assert isinstance(_lowerCamelCase   ,tf.keras.layers.Layer)
																						lowercase							=						model.get_bias()
																						assert name is None
																	else:
																						lowercase							=						model.get_output_embeddings()
																						assert x is None
																						lowercase							=						model.get_bias()
																						assert name is None




							def  A__      (				self):
												pass




							@slow
							def  A__      (				self):
												for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase							=						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  A__      (				self):
												pass







@require_tf
class 				lowercase		(					unittest.TestCase		):




							@unittest.skip('''Skip test until #12651 is resolved.''')
							@slow
							def  A__      (				self):
												lowercase							=						TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''')
												# fmt: off
												lowercase							=						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
												lowercase							=						[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>

												lowercase							=						model.generate(_lowerCamelCase   ,max_length=2_0_0   ,do_sample=_lowerCamelCase)
												self.assertListEqual(output_ids[0].numpy().tolist()   ,_lowerCamelCase)












"""simple docstring"""


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= a * self.x + b + rng.normal(scale=0.1,       size=(length,)  ).astype(np.floataa  )


   def __len__(	self :     str  ):

     '''simple docstring'''
     return self.length






   def __getitem__(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return {"x": self.x[i], "y": self.y[i]}





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a[0] + self.b[0]





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

  __A									= AutoTokenizer.from_pretrained('''bert-base-cased''' )
  __A									= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
  __A									= load_dataset('''csv'''		,       data_files=__UpperCamelCase )
  __A									= datasets['''train'''].unique('''label''' )

  __A									= {v: i for i, v in enumerate(__UpperCamelCase )}

  def tokenize_function(__UpperCamelCase ):
    # max_length=None => use the model max length (it's actually the default)
    __A									= tokenizer(
        examples['''sentence1''']		,       examples['''sentence2''']		,       truncation=__UpperCamelCase		,       max_length=__UpperCamelCase		,       padding='''max_length''' )
    if "label" in examples:
      __A									= [label_to_id[l] for l in examples['''label''']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  __A									= datasets.map(
      __UpperCamelCase		,       batched=__UpperCamelCase		,       remove_columns=['''sentence1''', '''sentence2''', '''label''']		,       )

  def collate_fn(__UpperCamelCase ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__UpperCamelCase		,       padding='''max_length'''		,       max_length=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

  # Instantiate dataloaders.
  __A									= DataLoader(tokenized_datasets['''train''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=2 )
  __A									= DataLoader(tokenized_datasets['''validation''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=1 )

  return train_dataloader, eval_dataloader




