"""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  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							)      ->      List[str]:
            snake_case_					       =					parent
            snake_case_					       =					13
            snake_case_					       =					7
            snake_case_					       =					30
            snake_case_					       =					self.seq_length + self.mem_len
            snake_case_					       =					15
            snake_case_					       =					True
            snake_case_					       =					True
            snake_case_					       =					99
            snake_case_					       =					[10, 50, 80]
            snake_case_					       =					32
            snake_case_					       =					32
            snake_case_					       =					4
            snake_case_					       =					8
            snake_case_					       =					128
            snake_case_					       =					2
            snake_case_					       =					2
            snake_case_					       =					None
            snake_case_					       =					1
            snake_case_					       =					0
            snake_case_					       =					3
            snake_case_					       =					self.vocab_size - 1
            snake_case_					       =					0.01







       def 		a_						( self)      ->      int:
            snake_case_					       =					ids_tensor([self.batch_size, self.seq_length],							self.vocab_size)
            snake_case_					       =					ids_tensor([self.batch_size, self.seq_length],							self.vocab_size)

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

            snake_case_					       =					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)      ->      int:
            random.seed(self.seed)
            tf.random.set_seed(self.seed)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					TFTransfoXLModel(lowerCAmelCase__)

            snake_case_       ,	snake_case_					       =					model(lowerCAmelCase__).to_tuple()

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

            snake_case_       ,	snake_case_					       =					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,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					TFTransfoXLLMHeadModel(lowerCAmelCase__)

            snake_case_       ,	snake_case_					       =					model(lowerCAmelCase__).to_tuple()

            snake_case_					       =					{'input_ids': input_ids_a, 'labels': lm_labels}
            snake_case_       ,	snake_case_					       =					model(lowerCAmelCase__).to_tuple()

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

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

            snake_case_       ,	snake_case_					       =					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,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      str:
            snake_case_					       =					TFTransfoXLForSequenceClassification(lowerCAmelCase__)
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.num_labels))







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.prepare_config_and_inputs()
            ((snake_case_)       ,	(snake_case_)       ,	(snake_case_)       ,	(snake_case_))					       =					config_and_inputs
            snake_case_					       =					{'input_ids': input_ids_a}
            return config, inputs_dict




@require_tf
class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       SCREAMING_SNAKE_CASE_			=      () if is_tf_available() else ()
       SCREAMING_SNAKE_CASE_			=      (
           {
               "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
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Dict:
            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)      ->      List[Any]:
            snake_case_					       =					TFTransfoXLModelTester(self)
            snake_case_					       =					ConfigTester(self,							config_class=lowerCAmelCase__,							d_embed=37)







       def 		a_						( self)      ->      Dict:
            self.config_tester.run_common_tests()







       def 		a_						( self)      ->      Dict:
            self.model_tester.set_seed()
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            self.model_tester.set_seed()
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__)







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()
            snake_case_					       =					[TFTransfoXLForSequenceClassification]

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







       def 		a_						( self)      ->      Optional[int]:
            # TODO JP: Make TransfoXL XLA compliant
            pass







       @slow
       def 		a_						( self)      ->      str:
            for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case_					       =					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)      ->      Union[str, Any]:
            pass




@require_tf
class  UpperCamelCase					(				unittest.TestCase   ):







       @unittest.skip('Skip test until #12651 is resolved.')
       @slow
       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
            # fmt: off
            snake_case_					       =					tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]],							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
            snake_case_					       =					[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # 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>

            snake_case_					       =					model.generate(lowerCAmelCase__,							max_length=200,							do_sample=lowerCAmelCase__)
            self.assertListEqual(output_ids[0].numpy().tolist(),							lowerCAmelCase__)









"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



