


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    :       Dict       ,							__lowerCamelCase    :       Any       ,							)     -> Optional[int]:






         """simple docstring"""







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





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






         """simple docstring"""







         UpperCAmelCase      					=  ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size							)
         UpperCAmelCase      					=  ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size							)

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

         UpperCAmelCase      					=  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 							_lowercase					(		self    :       Any							)     -> Union[str, Any]:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       str       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Any							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFTransfoXLModel(__lowerCamelCase							)

         UpperCAmelCase					,	UpperCAmelCase      					=  model(__lowerCamelCase							).to_tuple()

         UpperCAmelCase      					=  {"""input_ids""": input_ids_a, """mems""": mems_a}

         UpperCAmelCase					,	UpperCAmelCase      					=  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 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Any							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  TFTransfoXLLMHeadModel(__lowerCamelCase							)

         UpperCAmelCase					,	UpperCAmelCase      					=  model(__lowerCamelCase							).to_tuple()

         UpperCAmelCase      					=  {"""input_ids""": input_ids_a, """labels""": lm_labels}
         UpperCAmelCase					,	UpperCAmelCase      					=  model(__lowerCamelCase							).to_tuple()

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

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

         UpperCAmelCase					,	UpperCAmelCase      					=  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 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       int							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFTransfoXLForSequenceClassification(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       int							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  self.prepare_config_and_inputs()
         ((UpperCAmelCase)					,	(UpperCAmelCase)					,	(UpperCAmelCase)					,	(UpperCAmelCase))      					=  config_and_inputs
         UpperCAmelCase      					=  {"""input_ids""": input_ids_a}
         return config, inputs_dict






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





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """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 							_lowercase					(		self    :       str							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFTransfoXLModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(self       ,							config_class=__lowerCamelCase       ,							d_embed=3_7							)





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         self.config_tester.run_common_tests()





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         self.model_tester.set_seed()
         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*__lowerCamelCase							)





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






         """simple docstring"""







         self.model_tester.set_seed()
         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase      					=  [TFTransfoXLForSequenceClassification]

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





      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[Any]:






         """simple docstring"""







         pass





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[Any]:






         """simple docstring"""







         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  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 							_lowercase					(		self    :       Dict							)     -> List[Any]:






         """simple docstring"""







         pass






@require_tf
class 	__lowercase					(	unittest.TestCase	):





      @unittest.skip("""Skip test until #12651 is resolved."""							)
      @slow
      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""							)
         # fmt: off
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  [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>

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







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         if depth_multiplier <= 0:
            raise ValueError("""depth_multiplier must be greater than zero."""							)

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict([("""pixel_values""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         if self.task == "image-classification":
            return OrderedDict([("""logits""", {0: """batch"""})]							)
         else:
            return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



