

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      snake_case_				()      ->     List[Any]:
   __lowerCAmelCase  :      List[str]    =						"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
   __lowerCAmelCase  :      int    =						Image.open(requests.get(_UpperCAmelCase ,       stream=_UpperCAmelCase					).raw					).convert("""RGB"""					)

   return image





def      snake_case_				(__A					:						int					)      ->     Optional[int]:
   __lowerCAmelCase  :      Tuple    =						[]
   # 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      snake_case_				(__A					:						Union[str, Any] ,       __A					:						List[str] ,       __A					:						Dict					)      ->     Tuple:
   __lowerCAmelCase  :      str    =						dct.pop(_UpperCAmelCase					)
   __lowerCAmelCase  :      Optional[int]    =						val





def      snake_case_				(__A					:						Optional[int] ,       __A					:						Optional[Any]					)      ->     int:
   for i in range(config.vision_config.num_hidden_layers					):
      # read in original q and v biases
      __lowerCAmelCase  :      Any    =						state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''					)
      __lowerCAmelCase  :      Any    =						state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''					)

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





def      snake_case_				(__A					:						Any					)      ->     Union[str, Any]:
   __lowerCAmelCase  :      Optional[Any]    =						3_6_4 if "coco" in model_name else 2_2_4
   __lowerCAmelCase  :      Optional[int]    =						InstructBlipVisionConfig(image_size=_UpperCAmelCase					).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  :      int    =						TaConfig.from_pretrained("""google/flan-t5-xl""" ,       dense_act_fn="""gelu""" ,       bos_token_id=1					).to_dict()
   elif "t5-xxl" in model_name:
      __lowerCAmelCase  :      Dict    =						TaConfig.from_pretrained("""google/flan-t5-xxl""" ,       dense_act_fn="""gelu""" ,       bos_token_id=1					).to_dict()
   elif "vicuna-7b" in model_name:
      __lowerCAmelCase  :      Tuple    =						LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf""" ,       vocab_size=3_2_0_0_1					).to_dict()
   elif "vicuna-13b" in model_name:
      __lowerCAmelCase  :      Optional[int]    =						LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf""" ,       vocab_size=3_2_0_0_1					).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  :      Optional[int]    =						InstructBlipQFormerConfig(vocab_size=3_0_5_2_3					).to_dict()
   __lowerCAmelCase  :      int    =						InstructBlipConfig(vision_config=_UpperCAmelCase ,       text_config=_UpperCAmelCase ,       qformer_config=_UpperCAmelCase					)

   return config, image_size





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

   if "t5" in model_name:
      __lowerCAmelCase  :      int    =						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  :      Union[str, Any]    =						LlamaTokenizerFast.from_pretrained(
          """huggyllama/llama-7b""" ,       truncation_side="""left""" ,       bos_token="""</s>""" ,       unk_token="""</s>"""					)
      tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}					)

   __lowerCAmelCase  :      str    =						get_blipa_config(_UpperCAmelCase					)
   __lowerCAmelCase  :      Optional[Any]    =						InstructBlipForConditionalGeneration(_UpperCAmelCase					).eval()

   __lowerCAmelCase  :      str    =						{
       "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  :      Any    =						model_name_to_original[model_name]

   # load original model
   print("""Loading original model..."""					)
   __lowerCAmelCase  :      Tuple    =						"cuda:1" if torch.cuda.is_available() else "cpu"
   __lowerCAmelCase  :      str    =						"cuda:2" if torch.cuda.is_available() else "cpu"
   __lowerCAmelCase  :      str    =						load_model_and_preprocess(
       name=_UpperCAmelCase ,       model_type=_UpperCAmelCase ,       is_eval=_UpperCAmelCase ,       device=_UpperCAmelCase					)
   original_model.eval()
   print("""Done!"""					)

   # update state dict keys
   __lowerCAmelCase  :      Any    =						original_model.state_dict()
   __lowerCAmelCase  :      Any    =						create_rename_keys(_UpperCAmelCase					)
   for src, dest in rename_keys:
      rename_key(_UpperCAmelCase ,       _UpperCAmelCase ,       _UpperCAmelCase					)

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

   # read in qv biases
   read_in_q_v_bias(_UpperCAmelCase ,       _UpperCAmelCase					)

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

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

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

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

   original_model.to(_UpperCAmelCase					)
   hf_model.to(_UpperCAmelCase					)
   with torch.no_grad():
      if "vicuna" in model_name:
         __lowerCAmelCase  :      Optional[Any]    =						original_model({"""image""": original_pixel_values, """text_input""": [prompt]}					).logits
         __lowerCAmelCase  :      Optional[Any]    =						hf_model(**_UpperCAmelCase					).logits
      else:
         __lowerCAmelCase  :      str    =						original_model(
             {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}					).logits
         __lowerCAmelCase  :      Optional[Any]    =						tokenizer("""\n""" ,       return_tensors="""pt"""					).input_ids.to(_UpperCAmelCase					)
         __lowerCAmelCase  :      Dict    =						label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,       -1_0_0					)
         __lowerCAmelCase  :      List[str]    =						hf_model(**_UpperCAmelCase ,       labels=_UpperCAmelCase					).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  :      Any    =						1e-4 if "vicuna" in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device					) ,       _UpperCAmelCase ,       atol=_UpperCAmelCase					)
   print("""Looks ok!"""					)

   print("""Generating with original model..."""					)
   __lowerCAmelCase  :      List[Any]    =						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  :      Tuple    =						hf_model.generate(
       **_UpperCAmelCase ,       do_sample=_UpperCAmelCase ,       num_beams=5 ,       max_length=2_5_6 ,       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  :      int    =						2
   print("""Original generation:""" ,       _UpperCAmelCase					)
   __lowerCAmelCase  :      str    =						processor.batch_decode(_UpperCAmelCase ,       skip_special_tokens=_UpperCAmelCase					)
   __lowerCAmelCase  :      Dict    =						[text.strip() for text in output_text]
   print("""HF generation:""" ,       _UpperCAmelCase					)

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

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


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   __UpperCAmelCase								=			[
       "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""",
   )

   __UpperCAmelCase								=			parser.parse_args()

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





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel



@require_tf
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=MBartConfig
  lowerCamelCase   :      Optional[Any]										={}
  lowerCamelCase   :      Dict										="gelu"







  def __init__(      self			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=99	,							lowerCAmelCase			:   List[str]=32	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Any=37	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   List[Any]=0.1	,							lowerCAmelCase			:   Dict=20	,							lowerCAmelCase			:   Any=2	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   str=0	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      int    =						seq_length
     __lowerCAmelCase  :      Tuple    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Dict    =						num_hidden_layers
     __lowerCAmelCase  :      int    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Tuple    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						eos_token_id
     __lowerCAmelCase  :      Optional[Any]    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size   )
     __lowerCAmelCase  :      Optional[Any]    =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,							1   )
     __lowerCAmelCase  :      int    =						tf.concat([input_ids, eos_tensor]	,							axis=1   )

     __lowerCAmelCase  :      Any    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						self.config_cls(
         vocab_size=self.vocab_size	,							d_model=self.hidden_size	,							encoder_layers=self.num_hidden_layers	,							decoder_layers=self.num_hidden_layers	,							encoder_attention_heads=self.num_attention_heads	,							decoder_attention_heads=self.num_attention_heads	,							encoder_ffn_dim=self.intermediate_size	,							decoder_ffn_dim=self.intermediate_size	,							dropout=self.hidden_dropout_prob	,							attention_dropout=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							eos_token_ids=[2]	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							decoder_start_token_id=self.pad_token_id	,							**self.config_updates	,							)
     __lowerCAmelCase  :      Tuple    =						prepare_mbart_inputs_dict(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFMBartModel(config=lowerCAmelCase   ).get_decoder()
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      Optional[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      Union[str, Any]    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""head_mask"""]
     __lowerCAmelCase  :      Any    =						1

     # first forward pass
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							use_cache=lowerCAmelCase   )

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						outputs.to_tuple()
     __lowerCAmelCase  :      Union[str, Any]    =						past_key_values[1]






def      snake_case_				(__A					:						str ,       __A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Tuple=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[int]=None ,       __A					:						Optional[Any]=None ,       )      ->     int:
   if attention_mask is None:
      __lowerCAmelCase  :      Dict    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   if decoder_attention_mask is None:
      __lowerCAmelCase  :      str    =						tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape ,       dtype=tf.inta					),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,       config.pad_token_id					) ,       tf.inta					),
          ] ,       axis=-1 ,       )
   if head_mask is None:
      __lowerCAmelCase  :      Tuple    =						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      __lowerCAmelCase  :      List[Any]    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :      Any    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



@require_tf
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[int]										=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  lowerCamelCase   :      List[str]										=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
  lowerCamelCase   :      Union[str, Any]										=(
      {
          "conversational": TFMBartForConditionalGeneration,
          "feature-extraction": TFMBartModel,
          "summarization": TFMBartForConditionalGeneration,
          "text2text-generation": TFMBartForConditionalGeneration,
          "translation": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase   :      str										=True
  lowerCamelCase   :      Tuple										=False
  lowerCamelCase   :      Dict										=False







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






     """simple docstring"""







     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFMBartModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )






@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=[
      " UN Chief Says There Is No Military Solution in Syria",
  ]
  lowerCamelCase   :      Tuple										=[
      "Şeful ONU declară că nu există o soluţie militară în Siria",
  ]
  lowerCamelCase   :      List[Any]										="facebook/mbart-large-en-ro"







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Any:






     """simple docstring"""







     return AutoTokenizer.from_pretrained(self.model_name   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
     return model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.translate_src_text(**lowerCAmelCase   )
     self.assertListEqual(self.expected_text	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.tokenizer(self.src_text	,							**lowerCAmelCase	,							return_tensors="""tf"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						self.model.generate(
         model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2   )
     __lowerCAmelCase  :      List[str]    =						self.tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
     return generated_words







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()


