


"""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  :







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		List[Any]      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)
      a						:		List[Any]      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)

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

      a						:		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 	__snake_case							(					self				:    List[str]):
      random.seed(self.seed)
      tf.random.set_seed(self.seed)






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    int):
      a						:		List[str]      							=			TFTransfoXLModel(__UpperCAmelCase)

      a							,		a						:		List[str]      							=			model(__UpperCAmelCase).to_tuple()

      a						:		Optional[Any]      							=			{"input_ids": input_ids_a, "mems": mems_a}

      a							,		a						:		Union[str, Any]      							=			model(__UpperCAmelCase).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 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		List[Any]      							=			TFTransfoXLLMHeadModel(__UpperCAmelCase)

      a							,		a						:		Optional[Any]      							=			model(__UpperCAmelCase).to_tuple()

      a						:		Optional[int]      							=			{"input_ids": input_ids_a, "labels": lm_labels}
      a							,		a						:		Optional[int]      							=			model(__UpperCAmelCase).to_tuple()

      a							,		a						:		Optional[int]      							=			model([input_ids_a, mems_a]).to_tuple()

      a						:		Tuple      							=			{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

      a							,		a						:		List[str]      							=			model(__UpperCAmelCase).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 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    List[str]):
      a						:		Dict      							=			TFTransfoXLForSequenceClassification(__UpperCAmelCase)
      a						:		int      							=			model(__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels))






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Optional[Any]      							=			self.prepare_config_and_inputs()
      ((a)							,		(a)							,		(a)							,		(a))						:		List[Any]      							=			config_and_inputs
      a						:		List[str]      							=			{"input_ids": input_ids_a}
      return config, inputs_dict






@require_tf
class 						_A  (  _a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Any	=       (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    UpperCAmelCase       :							List[Any]	=       () if is_tf_available() else ()
    UpperCAmelCase       :							Tuple	=       (
        {
            """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
    UpperCAmelCase       :							Any	=       False
    UpperCAmelCase       :							int	=       False
    UpperCAmelCase       :							Dict	=       False
    UpperCAmelCase       :							Any	=       False






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[int]):
      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 	__snake_case							(					self				:    Any):
      a						:		Any      							=			TFTransfoXLModelTester(self)
      a						:		Any      							=			ConfigTester(self				, config_class=__UpperCAmelCase				, d_embed=37)






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






    def 	__snake_case							(					self				:    int):
      self.model_tester.set_seed()
      a						:		str      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      self.model_tester.set_seed()
      a						:		Tuple      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		List[str]      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple):
      a							,		a						:		List[str]      							=			self.model_tester.prepare_config_and_inputs_for_common()
      a						:		Optional[Any]      							=			[TFTransfoXLForSequenceClassification]

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






    def 	__snake_case							(					self				:    Optional[Any]):
      # TODO JP: Make TransfoXL XLA compliant
      pass






    @slow
    def 	__snake_case							(					self				:    str):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						:		Tuple      							=			TFTransfoXLModel.from_pretrained(__UpperCAmelCase)
        self.assertIsNotNone(__UpperCAmelCase)






    @unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss.")
    def 	__snake_case							(					self				:    Tuple):
      pass






@require_tf
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @unittest.skip("Skip test until #12651 is resolved.")
    @slow
    def 	__snake_case							(					self				:    List[str]):
      a						:		int      							=			TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
      # fmt: off
      a						:		Tuple      							=			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						:		Union[str, Any]      							=			[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[int]      							=			model.generate(__UpperCAmelCase				, max_length=200				, do_sample=__UpperCAmelCase)
      self.assertListEqual(output_ids[0].numpy().tolist()				, __UpperCAmelCase)














'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       tempfile.mkdtemp()

														# fmt: off
														lowerCamelCase__					:	int         =       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														lowerCamelCase__					:	Union[str, Any]         =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]     )
														with open(self.vocab_file			,							"""w"""			,							encoding="""utf-8"""     ) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

														lowerCamelCase__					:	Tuple         =       {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														lowerCamelCase__					:	Tuple         =       os.path.join(self.tmpdirname			,							UpperCamelCase__     )
														with open(self.image_processor_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					json.dump(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				List[str]     ):
														return BertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							**UpperCamelCase__:				Tuple     ):
														return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[Any]         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	Tuple         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_tokenizer()
														lowerCamelCase__					:	Dict         =       self.get_image_processor()

														lowerCamelCase__					:	Optional[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	int         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	List[Any]         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Tuple         =       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Optional[Any]         =       processor(images=UpperCamelCase__			,							return_tensors="""np"""     )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1e-2     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Dict         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Tuple         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

														# test if it raises when no input is passed
														with self.assertRaises(UpperCamelCase__     ):
																					processor()




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__					:	Union[str, Any]         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       """lower newer"""
														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	int         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							processor.model_input_names     )

