




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     XCLIPTextConfig()

     # derive patch size from model name
     UpperCAmelCase_     :				List[str]  =     model_name.find('''patch''' )
     UpperCAmelCase_     :				Optional[int]  =     int(model_name[start_idx + len('''patch''' ) : start_idx + len('''patch''' ) + 2] )
     UpperCAmelCase_     :				List[str]  =     XCLIPVisionConfig(patch_size=_lowercase					,		num_frames=_lowercase )

     if "large" in model_name:
          UpperCAmelCase_     :				Optional[int]  =     768
          UpperCAmelCase_     :				Dict  =     3072
          UpperCAmelCase_     :				Dict  =     12

          UpperCAmelCase_     :				str  =     1024
          UpperCAmelCase_     :				Any  =     4096
          UpperCAmelCase_     :				Union[str, Any]  =     16
          UpperCAmelCase_     :				Optional[Any]  =     24
          UpperCAmelCase_     :				List[Any]  =     768
          UpperCAmelCase_     :				Dict  =     3072

     if model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase_     :				int  =     336

     UpperCAmelCase_     :				List[Any]  =     XCLIPConfig.from_text_vision_configs(_lowercase					,		_lowercase )

     if "large" in model_name:
          UpperCAmelCase_     :				str  =     768

     return config







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if name == "token_embedding.weight":
          UpperCAmelCase_     :				Optional[int]  =     name.replace('''token_embedding.weight'''					,		'''text_model.embeddings.token_embedding.weight''' )
     if name == "positional_embedding":
          UpperCAmelCase_     :				Any  =     name.replace('''positional_embedding'''					,		'''text_model.embeddings.position_embedding.weight''' )
     if "ln_1" in name:
          UpperCAmelCase_     :				Any  =     name.replace('''ln_1'''					,		'''layer_norm1''' )
     if "ln_2" in name:
          UpperCAmelCase_     :				List[str]  =     name.replace('''ln_2'''					,		'''layer_norm2''' )
     if "c_fc" in name:
          UpperCAmelCase_     :				List[Any]  =     name.replace('''c_fc'''					,		'''fc1''' )
     if "c_proj" in name:
          UpperCAmelCase_     :				Any  =     name.replace('''c_proj'''					,		'''fc2''' )
     if name.startswith('''transformer.resblocks''' ):
          UpperCAmelCase_     :				int  =     name.replace('''transformer.resblocks'''					,		'''text_model.encoder.layers''' )
     if "attn.out_proj" in name and "message" not in name:
          UpperCAmelCase_     :				str  =     name.replace('''attn.out_proj'''					,		'''self_attn.out_proj''' )
     if "ln_final" in name:
          UpperCAmelCase_     :				List[str]  =     name.replace('''ln_final'''					,		'''text_model.final_layer_norm''' )
     # visual encoder
     if name == "visual.class_embedding":
          UpperCAmelCase_     :				List[str]  =     name.replace('''visual.class_embedding'''					,		'''vision_model.embeddings.class_embedding''' )
     if name == "visual.positional_embedding":
          UpperCAmelCase_     :				Optional[Any]  =     name.replace('''visual.positional_embedding'''					,		'''vision_model.embeddings.position_embedding.weight''' )
     if name.startswith('''visual.transformer.resblocks''' ):
          UpperCAmelCase_     :				Union[str, Any]  =     name.replace('''visual.transformer.resblocks'''					,		'''vision_model.encoder.layers''' )
     if "visual.conv1" in name:
          UpperCAmelCase_     :				Dict  =     name.replace('''visual.conv1'''					,		'''vision_model.embeddings.patch_embedding''' )
     if "visual.ln_pre" in name:
          UpperCAmelCase_     :				List[Any]  =     name.replace('''visual.ln_pre'''					,		'''vision_model.pre_layernorm''' )
     if "visual.ln_post" in name:
          UpperCAmelCase_     :				Optional[int]  =     name.replace('''visual.ln_post'''					,		'''vision_model.post_layernorm''' )
     if "visual.proj" in name:
          UpperCAmelCase_     :				str  =     name.replace('''visual.proj'''					,		'''visual_projection.weight''' )
     if "text_projection" in name:
          UpperCAmelCase_     :				Optional[int]  =     name.replace('''text_projection'''					,		'''text_projection.weight''' )
     # things on top
     if "prompts_visual_proj" in name:
          UpperCAmelCase_     :				List[str]  =     name.replace('''prompts_visual_proj'''					,		'''prompts_visual_projection''' )
     if "prompts_visual_ln" in name:
          UpperCAmelCase_     :				List[str]  =     name.replace('''prompts_visual_ln'''					,		'''prompts_visual_layernorm''' )
     # mit
     if name == "mit.positional_embedding":
          UpperCAmelCase_     :				int  =     name.replace('''positional'''					,		'''position''' )
     if name.startswith('''mit.resblocks''' ):
          UpperCAmelCase_     :				Optional[int]  =     name.replace('''mit.resblocks'''					,		'''mit.encoder.layers''' )
     # prompts generator
     if name.startswith('''prompts_generator.norm''' ):
          UpperCAmelCase_     :				Optional[int]  =     name.replace('''prompts_generator.norm'''					,		'''prompts_generator.layernorm''' )

     return name







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_     :				Any  =     orig_state_dict.pop(_lowercase )

          if "attn.in_proj" in key:
               UpperCAmelCase_     :				Optional[int]  =     key.split('''.''' )
               if key.startswith('''visual''' ):
                    UpperCAmelCase_     :				List[Any]  =     key_split[3]
                    UpperCAmelCase_     :				Optional[int]  =     config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              UpperCAmelCase_     :				List[str]  =     val[
                                  :dim, :
                              ]
                              UpperCAmelCase_     :				Optional[int]  =     val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase_     :				Optional[int]  =     val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase_     :				List[Any]  =     val[
                                  :dim
                              ]
                              UpperCAmelCase_     :				Any  =     val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase_     :				List[str]  =     val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              UpperCAmelCase_     :				Optional[int]  =     val[
                                  :dim, :
                              ]
                              UpperCAmelCase_     :				List[Any]  =     val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase_     :				List[str]  =     val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase_     :				List[Any]  =     val[:dim]
                              UpperCAmelCase_     :				List[Any]  =     val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase_     :				Optional[Any]  =     val[-dim:]
               elif key.startswith('''mit''' ):
                    UpperCAmelCase_     :				Optional[Any]  =     key_split[2]
                    UpperCAmelCase_     :				Tuple  =     config.vision_config.mit_hidden_size
                    if "weight" in key:
                         UpperCAmelCase_     :				Optional[Any]  =     val[:dim, :]
                         UpperCAmelCase_     :				Optional[int]  =     val[dim : dim * 2, :]
                         UpperCAmelCase_     :				Optional[int]  =     val[-dim:, :]
                    else:
                         UpperCAmelCase_     :				List[str]  =     val[:dim]
                         UpperCAmelCase_     :				List[Any]  =     val[dim : dim * 2]
                         UpperCAmelCase_     :				int  =     val[-dim:]
               else:
                    UpperCAmelCase_     :				List[str]  =     key_split[2]
                    UpperCAmelCase_     :				List[str]  =     config.text_config.hidden_size
                    if "weight" in key:
                         UpperCAmelCase_     :				Tuple  =     val[:dim, :]
                         UpperCAmelCase_     :				Any  =     val[
                             dim : dim * 2, :
                         ]
                         UpperCAmelCase_     :				str  =     val[-dim:, :]
                    else:
                         UpperCAmelCase_     :				Optional[int]  =     val[:dim]
                         UpperCAmelCase_     :				Tuple  =     val[
                             dim : dim * 2
                         ]
                         UpperCAmelCase_     :				List[Any]  =     val[-dim:]
          else:
               UpperCAmelCase_     :				int  =     rename_key(_lowercase )
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    UpperCAmelCase_     :				Dict  =     val.T
               UpperCAmelCase_     :				Tuple  =     val

     return orig_state_dict







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if num_frames == 8:
          UpperCAmelCase_     :				Tuple  =     '''eating_spaghetti_8_frames.npy'''
     elif num_frames == 16:
          UpperCAmelCase_     :				Union[str, Any]  =     '''eating_spaghetti.npy'''
     elif num_frames == 32:
          UpperCAmelCase_     :				Optional[Any]  =     '''eating_spaghetti_32_frames.npy'''
     UpperCAmelCase_     :				List[Any]  =     hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''					,		filename=_lowercase					,		repo_type='''dataset'''					,		)
     UpperCAmelCase_     :				Union[str, Any]  =     np.load(_lowercase )
     return list(_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase=None					,		_lowercase=False ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     {
         # fully supervised kinetics-400 checkpoints
         '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
         '''xclip-base-patch32-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
         ),
         '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
         '''xclip-base-patch16-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
         ),
         '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
         '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
         # fully supervised kinetics-600 checkpoints
         '''xclip-base-patch16-kinetics-600''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
         ),
         '''xclip-base-patch16-kinetics-600-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
         ),
         '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
         # few shot
         '''xclip-base-patch16-hmdb-2-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
         ),
         '''xclip-base-patch16-hmdb-4-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
         ),
         '''xclip-base-patch16-hmdb-8-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
         ),
         '''xclip-base-patch16-hmdb-16-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
         ),
         '''xclip-base-patch16-ucf-2-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
         ),
         '''xclip-base-patch16-ucf-4-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
         ),
         '''xclip-base-patch16-ucf-8-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
         ),
         '''xclip-base-patch16-ucf-16-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
         ),
         # zero shot
         '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
     }

     UpperCAmelCase_     :				Optional[int]  =     model_to_url[model_name]
     UpperCAmelCase_     :				Dict  =     8
     if "16-frames" in model_name:
          UpperCAmelCase_     :				List[Any]  =     16
     elif "shot" in model_name:
          UpperCAmelCase_     :				Tuple  =     32

     UpperCAmelCase_     :				Any  =     get_xclip_config(_lowercase					,		_lowercase )
     UpperCAmelCase_     :				str  =     XCLIPModel(_lowercase )
     model.eval()

     if "drive" in checkpoint_url:
          UpperCAmelCase_     :				int  =     '''pytorch_model.bin'''
          gdown.cached_download(_lowercase					,		_lowercase					,		quiet=_lowercase )
          UpperCAmelCase_     :				Union[str, Any]  =     torch.load(_lowercase					,		map_location='''cpu''' )['''model''']
     else:
          UpperCAmelCase_     :				Optional[int]  =     torch.hub.load_state_dict_from_url(_lowercase )['''model''']

     UpperCAmelCase_     :				Dict  =     convert_state_dict(_lowercase					,		_lowercase )

     UpperCAmelCase_     :				int  =     XCLIPModel(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     model.load_state_dict(_lowercase					,		strict=_lowercase )
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     UpperCAmelCase_     :				Optional[Any]  =     336 if model_name == '''xclip-large-patch14-16-frames''' else 224
     UpperCAmelCase_     :				List[str]  =     VideoMAEImageProcessor(size=_lowercase )
     UpperCAmelCase_     :				Tuple  =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
     UpperCAmelCase_     :				Tuple  =     CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32''' )
     UpperCAmelCase_     :				List[Any]  =     XCLIPProcessor(image_processor=_lowercase					,		tokenizer=_lowercase )

     UpperCAmelCase_     :				Dict  =     prepare_video(_lowercase )
     UpperCAmelCase_     :				List[str]  =     processor(
         text=['''playing sports''', '''eating spaghetti''', '''go shopping''']					,		videos=_lowercase					,		return_tensors='''pt'''					,		padding=_lowercase )

     print('''Shape of pixel values:'''					,		inputs.pixel_values.shape )

     with torch.no_grad():
          UpperCAmelCase_     :				str  =     model(**_lowercase )

     # Verify outputs
     UpperCAmelCase_     :				Dict  =     outputs.logits_per_video
     UpperCAmelCase_     :				Union[str, Any]  =     logits_per_video.softmax(dim=1 )
     print('''Probs:'''					,		_lowercase )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          UpperCAmelCase_     :				Tuple  =     torch.tensor([[0.0019, 0.9951, 0.0030]] )
     elif model_name == "xclip-base-patch32-16-frames":
          UpperCAmelCase_     :				Any  =     torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]] )
     elif model_name == "xclip-base-patch16":
          UpperCAmelCase_     :				Tuple  =     torch.tensor([[0.0083, 0.9681, 0.0236]] )
     elif model_name == "xclip-base-patch16-16-frames":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]] )
     elif model_name == "xclip-large-patch14":
          UpperCAmelCase_     :				str  =     torch.tensor([[0.0062, 0.9864, 0.0075]] )
     elif model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]] )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[0.0555, 0.8914, 0.0531]] )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]] )
     elif model_name == "xclip-large-patch14-kinetics-600":
          UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([[0.0036, 0.9920, 0.0045]] )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          UpperCAmelCase_     :				List[Any]  =     torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          UpperCAmelCase_     :				str  =     torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          UpperCAmelCase_     :				Dict  =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([[0.0027, 0.9904, 0.0070]] )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          UpperCAmelCase_     :				Any  =     torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]] )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]] )
     else:
          raise ValueError(f'''Model name {model_name} not supported''' )
     assert torch.allclose(_lowercase					,		_lowercase					,		atol=1E-3 )
     print('''Looks ok!''' )

     if pytorch_dump_folder_path is not None:
          print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
          model.save_pretrained(_lowercase )

     if push_to_hub:
          print('''Pushing model, processor and slow tokenizer files to the hub...''' )
          model.push_to_hub(_lowercase					,		organization='''nielsr''' )
          processor.push_to_hub(_lowercase					,		organization='''nielsr''' )
          slow_tokenizer.push_to_hub(_lowercase					,		organization='''nielsr''' )


if __name__ == "__main__":
     __a							     =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='xclip-base-patch32',
         type=str,
         help='Name of the model.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     __a							     =					parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   BlenderbotSmallConfig
      UpperCAmelCase__							=   {}
      UpperCAmelCase__							=   """gelu"""


      def __init__(		self   ,				a__   ,				a__=13   ,				a__=7   ,				a__=True   ,				a__=False   ,				a__=99   ,				a__=32   ,				a__=2   ,				a__=4   ,				a__=37   ,				a__=0.1   ,				a__=0.1   ,				a__=20   ,				a__=2   ,				a__=1   ,				a__=0   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		parent
             _lowerCamelCase				:  Any						=		batch_size
             _lowerCamelCase				:  str						=		seq_length
             _lowerCamelCase				:  Optional[int]						=		is_training
             _lowerCamelCase				:  int						=		use_labels
             _lowerCamelCase				:  List[str]						=		vocab_size
             _lowerCamelCase				:  List[Any]						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  int						=		intermediate_size

             _lowerCamelCase				:  int						=		hidden_dropout_prob
             _lowerCamelCase				:  Tuple						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Dict						=		max_position_embeddings
             _lowerCamelCase				:  str						=		eos_token_id
             _lowerCamelCase				:  Any						=		pad_token_id
             _lowerCamelCase				:  str						=		bos_token_id


      def 					__snake_case      (		self):



             """simple docstring"""





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

             _lowerCamelCase				:  List[str]						=		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				:  Optional[int]						=		prepare_blenderbot_small_inputs_dict(a__   ,				a__   ,				a__)
             return config, inputs_dict



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		TFBlenderbotSmallModel(config=a__).get_decoder()
             _lowerCamelCase				:  Dict						=		inputs_dict['''input_ids''']

             _lowerCamelCase				:  int						=		input_ids[:1, :]
             _lowerCamelCase				:  Union[str, Any]						=		inputs_dict['''attention_mask'''][:1, :]
             _lowerCamelCase				:  List[str]						=		inputs_dict['''head_mask''']
             _lowerCamelCase				:  Optional[int]						=		1

             # first forward pass
             _lowerCamelCase				:  Union[str, Any]						=		model(a__   ,				attention_mask=a__   ,				head_mask=a__   ,				use_cache=a__)

             _lowerCamelCase,	_lowerCamelCase				:  str						=		outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _lowerCamelCase				:  Any						=		ids_tensor((self.batch_size, 3)   ,				config.vocab_size)
             _lowerCamelCase				:  Optional[Any]						=		tf.cast(ids_tensor((self.batch_size, 3)   ,				2)   ,				tf.inta)

             # append to next input_ids and
             _lowerCamelCase				:  Any						=		tf.concat([input_ids, next_tokens]   ,				axis=-1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([attention_mask, next_attn_mask]   ,				axis=-1)

             _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__)[0]
             _lowerCamelCase				:  str						=		model(a__   ,				attention_mask=a__   ,				past_key_values=a__)[0]

             self.parent.assertEqual(next_tokens.shape[1]   ,				output_from_past.shape[1])

             # select random slice
             _lowerCamelCase				:  int						=		int(ids_tensor((1,)   ,				output_from_past.shape[-1]))
             _lowerCamelCase				:  Any						=		output_from_no_past[:, -3:, random_slice_idx]
             _lowerCamelCase				:  int						=		output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(a__   ,				a__   ,				rtol=1e-3)




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     ):
       if attention_mask is None:
              _lowerCamelCase				:  List[Any]						=		tf.cast(tf.math.not_equal(lowercase_			,     config.pad_token_id   )			,     tf.inta   )
       if decoder_attention_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		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				:  List[str]						=		tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _lowerCamelCase				:  Optional[int]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _lowerCamelCase				:  Union[str, 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       __A							(   lowerCamelCase__			,lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      UpperCAmelCase__							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      UpperCAmelCase__							=   (
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		TFBlenderbotSmallModelTester(self)
             _lowerCamelCase				:  Optional[int]						=		ConfigTester(self   ,				config_class=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.config_tester.run_common_tests()



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_tokenizers
@require_tf
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   [
          """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
          """  i'm going to throw up.\nand why is that?"""
      ]
      UpperCAmelCase__							=   """facebook/blenderbot_small-90M"""


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.tokenizer(self.src_text   ,				return_tensors='''tf''')
             _lowerCamelCase				:  str						=		self.model.generate(
                 model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=a__   ,				)
             _lowerCamelCase				:  Tuple						=		self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=a__)[0]
             assert generated_words in (
                 "i don't know. i just feel like i'm going to throw up. it's not fun.",
                 "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                 "i'm not sure. i just feel like i've been in a bad situation.",
             )

