


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE       :		List[Any]         =				{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Blip2Model",
       "Blip2QFormerModel",
       "Blip2PreTrainedModel",
       "Blip2ForConditionalGeneration",
       "Blip2VisionModel",
   ]

if TYPE_CHECKING:
   from .configuration_blip_a import (
       BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlipaConfig,
       BlipaQFormerConfig,
       BlipaVisionConfig,
   )
   from .processing_blip_a import BlipaProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip_a import (
          BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipaForConditionalGeneration,
          BlipaModel,
          BlipaPreTrainedModel,
          BlipaQFormerModel,
          BlipaVisionModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE       :		str         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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					:


   '''simple docstring'''







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

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

        lowercase_       :int     =       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		):
        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :List[Any]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Dict     =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

        lowercase_    ,      lowercase_       :int     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Optional[int]     =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        lowercase_    ,      lowercase_       :Optional[int]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_    ,      lowercase_       :Tuple     =       model([input_ids_a, mems_a]		).to_tuple()

        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        lowercase_    ,      lowercase_       :Union[str, Any]     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       TFTransfoXLForSequenceClassification(UpperCamelCase_		)
        lowercase_       :int     =       model(UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        ((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))       :Tuple     =       config_and_inputs
        lowercase_       :Dict     =       {'''input_ids''': input_ids_a}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   lowercase					:	Dict									=() if is_tf_available() else ()
   lowercase					:	List[str]									=(
       {
           """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					:	Optional[int]									=False
   lowercase					:	Tuple									=False
   lowercase					:	Dict									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        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		):
        lowercase_       :Union[str, Any]     =       TFTransfoXLModelTester(self		)
        lowercase_       :str     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							d_embed=37		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :List[str]     =       [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             lowercase_       :Dict     =       model_class(UpperCamelCase_		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                  lowercase_       :str     =       model.get_output_embeddings()
                  assert isinstance(UpperCamelCase_   ,							tf.keras.layers.Layer		)
                  lowercase_       :Optional[int]     =       model.get_bias()
                  assert name is None
             else:
                  lowercase_       :List[Any]     =       model.get_output_embeddings()
                  assert x is None
                  lowercase_       :Dict     =       model.get_bias()
                  assert name is None
   def 		UpperCamelCase  (     self		):
        # TODO JP: Make TransfoXL XLA compliant
        pass
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Dict     =       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 		UpperCamelCase  (     self		):
        pass






@require_tf
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @unittest.skip('''Skip test until #12651 is resolved.'''		)
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        lowercase_       :List[str]     =       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
        lowercase_       :List[Any]     =       [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>

        lowercase_       :Any     =       model.generate(UpperCamelCase_   ,							max_length=200   ,							do_sample=UpperCamelCase_		)
        self.assertListEqual(output_ids[0].numpy().tolist()   ,							UpperCamelCase_		)

