"""simple docstring"""




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 							SCREAMING_SNAKE_CASE  (    ) ->   int:
  _lowerCAmelCase		: Optional[Any]						    =  """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
  _lowerCAmelCase		: List[Any]						    =  Image.open(requests.get(_lowerCamelCase    ,stream=_lowerCamelCase			).raw			).convert("""RGB"""			)

  return image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple			) ->   str:
  _lowerCAmelCase		: int						    =  []
  # fmt: off

  # vision encoder
  rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")			)
  rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")			)
  rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")			)
  rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")			)
  rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")			)
  rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")			)

  for i in range(config.vision_config.num_hidden_layers			):
    rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",)			)
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")			)
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")			)

  # QFormer
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.embeddings.layernorm.weight""")			)
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")			)

  # fmt: on
  return rename_keys





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int    ,_lowerCamelCase		: List[Any]			) ->   List[Any]:
  _lowerCAmelCase		: str						    =  dct.pop(_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  val





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[str]			) ->   str:
  for i in range(config.vision_config.num_hidden_layers			):
    # read in original q and v biases
    _lowerCAmelCase		: int						    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"			)
    _lowerCAmelCase		: List[Any]						    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"			)

    # next, set bias in the state dict
    _lowerCAmelCase		: Any						    =  torch.cat((q_bias, torch.zeros_like(_lowerCamelCase    ,requires_grad=_lowerCamelCase			), v_bias)			)
    _lowerCAmelCase		: Optional[Any]						    =  qkv_bias





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   List[str]:
  _lowerCAmelCase		: int						    =  364 if """coco""" in model_name else 224
  _lowerCAmelCase		: int						    =  InstructBlipVisionConfig(image_size=_lowerCamelCase			).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "t5-xl" in model_name:
    _lowerCAmelCase		: str						    =  TaConfig.from_pretrained("""google/flan-t5-xl"""    ,dense_act_fn="""gelu"""    ,bos_token_id=1			).to_dict()
  elif "t5-xxl" in model_name:
    _lowerCAmelCase		: Optional[Any]						    =  TaConfig.from_pretrained("""google/flan-t5-xxl"""    ,dense_act_fn="""gelu"""    ,bos_token_id=1			).to_dict()
  elif "vicuna-7b" in model_name:
    _lowerCAmelCase		: Dict						    =  LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""    ,vocab_size=32001			).to_dict()
  elif "vicuna-13b" in model_name:
    _lowerCAmelCase		: Tuple						    =  LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""    ,vocab_size=32001			).to_dict()
  else:
    raise ValueError("""Model name not supported"""			)

  # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
  _lowerCAmelCase		: List[Any]						    =  InstructBlipQFormerConfig(vocab_size=30523			).to_dict()
  _lowerCAmelCase		: Optional[Any]						    =  InstructBlipConfig(vision_config=_lowerCamelCase    ,text_config=_lowerCamelCase    ,qformer_config=_lowerCamelCase			)

  return config, image_size





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: List[Any]=None    ,_lowerCamelCase		: Any=False			) ->   Any:
  _lowerCAmelCase		: int						    =  AutoTokenizer.from_pretrained("""bert-base-uncased"""    ,truncation_side="""left"""			)
  qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}			)

  if "t5" in model_name:
    _lowerCAmelCase		: str						    =  TaTokenizerFast.from_pretrained("""google/flan-t5-xl"""    ,truncation_side="""left"""			)
  elif "vicuna" in model_name:
    # the following was used in the original implementation:
    # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
    # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
    # tokenizer.add_special_tokens({"bos_token": "</s>"})
    # tokenizer.add_special_tokens({"eos_token": "</s>"})
    # tokenizer.add_special_tokens({"unk_token": "</s>"})
    _lowerCAmelCase		: Any						    =  LlamaTokenizerFast.from_pretrained(
        """huggyllama/llama-7b"""    ,truncation_side="""left"""    ,bos_token="""</s>"""    ,unk_token="""</s>"""			)
    tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}			)

  _lowerCAmelCase		: Dict						    =  get_blipa_config(_lowerCamelCase			)
  _lowerCAmelCase		: Union[str, Any]						    =  InstructBlipForConditionalGeneration(_lowerCamelCase			).eval()

  _lowerCAmelCase		: List[Any]						    =  {
      """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
      """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
      """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
      """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
  }

  _lowerCAmelCase		: Union[str, Any]						    =  model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""			)
  _lowerCAmelCase		: Union[str, Any]						    =  """cuda:1""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase		: Optional[int]						    =  """cuda:2""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase		: Tuple						    =  load_model_and_preprocess(
      name=_lowerCamelCase    ,model_type=_lowerCamelCase    ,is_eval=_lowerCamelCase    ,device=_lowerCamelCase			)
  original_model.eval()
  print("""Done!"""			)

  # update state dict keys
  _lowerCAmelCase		: List[str]						    =  original_model.state_dict()
  _lowerCAmelCase		: Any						    =  create_rename_keys(_lowerCamelCase			)
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    _lowerCAmelCase		: int						    =  state_dict.pop(_lowerCamelCase			)
    if key.startswith("""Qformer.bert"""			):
      _lowerCAmelCase		: Tuple						    =  key.replace("""Qformer.bert"""    ,"""qformer"""			)
    if "attention.self" in key:
      _lowerCAmelCase		: int						    =  key.replace("""self"""    ,"""attention"""			)
    if "llm_proj" in key:
      _lowerCAmelCase		: Dict						    =  key.replace("""llm_proj"""    ,"""language_projection"""			)
    if "t5_proj" in key:
      _lowerCAmelCase		: Dict						    =  key.replace("""t5_proj"""    ,"""language_projection"""			)
    if key.startswith("""llm_model"""			):
      _lowerCAmelCase		: str						    =  key.replace("""llm_model"""    ,"""language_model"""			)
    if key.startswith("""t5"""			):
      _lowerCAmelCase		: Optional[Any]						    =  key.replace("""t5"""    ,"""language"""			)
    _lowerCAmelCase		: Dict						    =  val

  # read in qv biases
  read_in_q_v_bias(_lowerCamelCase    ,_lowerCamelCase			)

  # note: weights get loaded in torch.float32 by default
  hf_model.load_state_dict(_lowerCamelCase    ,strict=_lowerCamelCase			)

  _lowerCAmelCase		: int						    =  load_demo_image()
  _lowerCAmelCase		: Dict						    =  """What is unusual about this image?"""

  # create processor
  _lowerCAmelCase		: int						    =  BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}    ,image_mean=_lowerCamelCase    ,image_std=_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  InstructBlipProcessor(
      image_processor=_lowerCamelCase    ,tokenizer=_lowerCamelCase    ,qformer_tokenizer=_lowerCamelCase    ,)
  _lowerCAmelCase		: Optional[Any]						    =  processor(images=_lowerCamelCase    ,text=_lowerCamelCase    ,return_tensors="""pt"""			).to(_lowerCamelCase			)

  # make sure processor creates exact same pixel values
  _lowerCAmelCase		: Any						    =  vis_processors["""eval"""](_lowerCamelCase			).unsqueeze(0			).to(_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  inputs.pixel_values
  assert torch.allclose(original_pixel_values.to(pixel_values.device			)    ,_lowerCamelCase			)

  original_model.to(_lowerCamelCase			)
  hf_model.to(_lowerCamelCase			)
  with torch.no_grad():
    if "vicuna" in model_name:
      _lowerCAmelCase		: Dict						    =  original_model({"""image""": original_pixel_values, """text_input""": [prompt]}			).logits
      _lowerCAmelCase		: str						    =  hf_model(**_lowerCamelCase			).logits
    else:
      _lowerCAmelCase		: Dict						    =  original_model(
          {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}			).logits
      _lowerCAmelCase		: Union[str, Any]						    =  tokenizer("""\n"""    ,return_tensors="""pt"""			).input_ids.to(_lowerCamelCase			)
      _lowerCAmelCase		: List[Any]						    =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id    ,-100			)
      _lowerCAmelCase		: str						    =  hf_model(**_lowerCamelCase    ,labels=_lowerCamelCase			).logits

  print("""First values of original logits:"""    ,original_logits[0, :3, :3]			)
  print("""First values of HF logits:"""    ,logits[0, :3, :3]			)

  # assert values
  assert original_logits.shape == logits.shape
  _lowerCAmelCase		: Optional[Any]						    =  1e-4 if """vicuna""" in model_name else 1e-5
  assert torch.allclose(original_logits.to(logits.device			)    ,_lowerCamelCase    ,atol=_lowerCamelCase			)
  print("""Looks ok!"""			)

  print("""Generating with original model..."""			)
  _lowerCAmelCase		: str						    =  original_model.generate({"""image""": original_pixel_values, """prompt""": prompt}    ,num_beams=5			)

  # important: we need to cast the weights of the HF model to the appropriate type
  print("""Generating with HF model..."""			)
  _lowerCAmelCase		: int						    =  hf_model.generate(
      **_lowerCamelCase    ,do_sample=_lowerCamelCase    ,num_beams=5    ,max_length=256    ,min_length=1    ,top_p=0.9    ,repetition_penalty=1.5    ,length_penalty=1.0    ,temperature=1    ,)
  if "vicuna" in model_name:
    # convert output id 0 to 2 (eos_token_id)
    # TODO add this in the generate method?
    _lowerCAmelCase		: List[str]						    =  2
  print("""Original generation:"""    ,_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  processor.batch_decode(_lowerCamelCase    ,skip_special_tokens=_lowerCamelCase			)
  _lowerCAmelCase		: Union[str, Any]						    =  [text.strip() for text in output_text]
  print("""HF generation:"""    ,_lowerCamelCase			)

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(_lowerCamelCase			)
    hf_model.save_pretrained(_lowerCamelCase			)

  if push_to_hub:
    processor.push_to_hub(f"Salesforce/{model_name}"			)
    hf_model.push_to_hub(f"Salesforce/{model_name}"			)


if __name__ == "__main__":
     _a   :	Tuple 					=     argparse.ArgumentParser()
     _a   :	str 					=     [
         'instructblip-vicuna-7b',
         'instructblip-vicuna-13b',
         'instructblip-flan-t5-xl',
         'instructblip-flan-t5-xxl',
     ]
     parser.add_argument(
         '--model_name',
         default='instructblip-flan-t5-xl',
         choices=choices,
         type=str,
         help='Path to hf config.json of model to convert',
     )
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether to push the model and processor to the hub after converting',
     )

     _a   :	Any 					=     parser.parse_args()

     convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""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							:






     def __init__(	self       ,  a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Tuple						    =  13
       _lowerCAmelCase		: Tuple						    =  7
       _lowerCAmelCase		: Any						    =  30
       _lowerCAmelCase		: Optional[int]						    =  self.seq_length + self.mem_len
       _lowerCAmelCase		: Dict						    =  15
       _lowerCAmelCase		: List[Any]						    =  True
       _lowerCAmelCase		: Any						    =  True
       _lowerCAmelCase		: List[str]						    =  99
       _lowerCAmelCase		: List[Any]						    =  [10, 50, 80]
       _lowerCAmelCase		: Tuple						    =  32
       _lowerCAmelCase		: int						    =  32
       _lowerCAmelCase		: Dict						    =  4
       _lowerCAmelCase		: List[str]						    =  8
       _lowerCAmelCase		: Tuple						    =  128
       _lowerCAmelCase		: Any						    =  2
       _lowerCAmelCase		: List[Any]						    =  2
       _lowerCAmelCase		: List[Any]						    =  None
       _lowerCAmelCase		: Optional[Any]						    =  1
       _lowerCAmelCase		: Tuple						    =  0
       _lowerCAmelCase		: List[Any]						    =  3
       _lowerCAmelCase		: Optional[int]						    =  self.vocab_size - 1
       _lowerCAmelCase		: Dict						    =  0.0_1






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)
       _lowerCAmelCase		: Optional[Any]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

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

       _lowerCAmelCase		: Union[str, Any]						    =  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__						):
       _lowerCAmelCase		: Optional[int]						    =  TFTransfoXLModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).to_tuple()

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

       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model(a__						).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__						):
       _lowerCAmelCase		: int						    =  TFTransfoXLLMHeadModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Dict						    =  {"""input_ids""": input_ids_a, """labels""": lm_labels}
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  model([input_ids_a, mems_a]						).to_tuple()

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

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).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__						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLForSequenceClassification(a__						)
       _lowerCAmelCase		: int						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.prepare_config_and_inputs()
       ((_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase))		: Dict						    =  config_and_inputs
       _lowerCAmelCase		: List[Any]						    =  {"""input_ids""": input_ids_a}
       return config, inputs_dict







@require_tf
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _UpperCamelCase							:    Tuple	      =	() if is_tf_available() else ()
     _UpperCamelCase							:    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
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    Tuple	      =	False
     _UpperCamelCase							:    Any	      =	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						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLModelTester(self						)
       _lowerCAmelCase		: List[Any]						    =  ConfigTester(self       ,  config_class=a__       ,  d_embed=37						)






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






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*a__						)






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: List[Any]						    =  [TFTransfoXLForSequenceClassification]

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






     def 					__A						(	self						):
       # TODO JP: Make TransfoXL XLA compliant
       pass






     @slow
     def 					__A						(	self						):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Union[str, Any]						    =  TFTransfoXLModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)






     @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 		__A							(				unittest.TestCase	):






     @unittest.skip("""Skip test until #12651 is resolved."""						)
     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
       # fmt: off
       _lowerCAmelCase		: List[str]						    =  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
       _lowerCAmelCase		: List[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>

       _lowerCAmelCase		: Tuple						    =  model.generate(a__       ,  max_length=200       ,  do_sample=a__						)
       self.assertListEqual(output_ids[0].numpy().tolist()       ,  a__						)




