




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[str]      )  ->							Dict:
    UpperCAmelCase_							 =     CLIPSegTextConfig()
    UpperCAmelCase_							 =     CLIPSegVisionConfig(patch_size=16      )

    UpperCAmelCase_							 =     True if '''refined''' in model_name else False
    UpperCAmelCase_							 =     16 if '''rd16''' in model_name else 64

    UpperCAmelCase_							 =     CLIPSegConfig.from_text_vision_configs(
        __UpperCamelCase       ,     __UpperCamelCase       ,     use_complex_transposed_convolution=__UpperCamelCase       ,     reduce_dim=__UpperCamelCase       ,     )
    return config




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Optional[int]      )  ->							Any:
    # update prefixes
    if "clip_model" in name:
        UpperCAmelCase_							 =     name.replace('''clip_model'''       ,     '''clip'''      )
    if "transformer" in name:
        if "visual" in name:
            UpperCAmelCase_							 =     name.replace('''visual.transformer'''       ,     '''vision_model'''      )
        else:
            UpperCAmelCase_							 =     name.replace('''transformer'''       ,     '''text_model'''      )
    if "resblocks" in name:
        UpperCAmelCase_							 =     name.replace('''resblocks'''       ,     '''encoder.layers'''      )
    if "ln_1" in name:
        UpperCAmelCase_							 =     name.replace('''ln_1'''       ,     '''layer_norm1'''      )
    if "ln_2" in name:
        UpperCAmelCase_							 =     name.replace('''ln_2'''       ,     '''layer_norm2'''      )
    if "c_fc" in name:
        UpperCAmelCase_							 =     name.replace('''c_fc'''       ,     '''fc1'''      )
    if "c_proj" in name:
        UpperCAmelCase_							 =     name.replace('''c_proj'''       ,     '''fc2'''      )
    if "attn" in name and "self" not in name:
        UpperCAmelCase_							 =     name.replace('''attn'''       ,     '''self_attn'''      )
    # text encoder
    if "token_embedding" in name:
        UpperCAmelCase_							 =     name.replace('''token_embedding'''       ,     '''text_model.embeddings.token_embedding'''      )
    if "positional_embedding" in name and "visual" not in name:
        UpperCAmelCase_							 =     name.replace('''positional_embedding'''       ,     '''text_model.embeddings.position_embedding.weight'''      )
    if "ln_final" in name:
        UpperCAmelCase_							 =     name.replace('''ln_final'''       ,     '''text_model.final_layer_norm'''      )
    # vision encoder
    if "visual.class_embedding" in name:
        UpperCAmelCase_							 =     name.replace('''visual.class_embedding'''       ,     '''vision_model.embeddings.class_embedding'''      )
    if "visual.conv1" in name:
        UpperCAmelCase_							 =     name.replace('''visual.conv1'''       ,     '''vision_model.embeddings.patch_embedding'''      )
    if "visual.positional_embedding" in name:
        UpperCAmelCase_							 =     name.replace('''visual.positional_embedding'''       ,     '''vision_model.embeddings.position_embedding.weight'''      )
    if "visual.ln_pre" in name:
        UpperCAmelCase_							 =     name.replace('''visual.ln_pre'''       ,     '''vision_model.pre_layrnorm'''      )
    if "visual.ln_post" in name:
        UpperCAmelCase_							 =     name.replace('''visual.ln_post'''       ,     '''vision_model.post_layernorm'''      )
    # projection layers
    if "visual.proj" in name:
        UpperCAmelCase_							 =     name.replace('''visual.proj'''       ,     '''visual_projection.weight'''      )
    if "text_projection" in name:
        UpperCAmelCase_							 =     name.replace('''text_projection'''       ,     '''text_projection.weight'''      )
    # decoder
    if "trans_conv" in name:
        UpperCAmelCase_							 =     name.replace('''trans_conv'''       ,     '''transposed_convolution'''      )
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
        UpperCAmelCase_							 =     '''decoder.''' + name
    if "blocks" in name:
        UpperCAmelCase_							 =     name.replace('''blocks'''       ,     '''decoder.layers'''      )
    if "linear1" in name:
        UpperCAmelCase_							 =     name.replace('''linear1'''       ,     '''mlp.fc1'''      )
    if "linear2" in name:
        UpperCAmelCase_							 =     name.replace('''linear2'''       ,     '''mlp.fc2'''      )
    if "norm1" in name and "layer_" not in name:
        UpperCAmelCase_							 =     name.replace('''norm1'''       ,     '''layer_norm1'''      )
    if "norm2" in name and "layer_" not in name:
        UpperCAmelCase_							 =     name.replace('''norm2'''       ,     '''layer_norm2'''      )

    return name




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Union[str, Any]      )  ->							Tuple:
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_							 =     orig_state_dict.pop(__UpperCamelCase      )

        if key.startswith('''clip_model'''      ) and "attn.in_proj" in key:
            UpperCAmelCase_							 =     key.split('''.'''      )
            if "visual" in key:
                UpperCAmelCase_							 =     int(key_split[4]      )
                UpperCAmelCase_							 =     config.vision_config.hidden_size
                UpperCAmelCase_							 =     '''vision_model'''
            else:
                UpperCAmelCase_							 =     int(key_split[3]      )
                UpperCAmelCase_							 =     config.text_config.hidden_size
                UpperCAmelCase_							 =     '''text_model'''

            if "weight" in key:
                UpperCAmelCase_							 =     val[:dim, :]
                UpperCAmelCase_							 =     val[
                    dim : dim * 2, :
                ]
                UpperCAmelCase_							 =     val[-dim:, :]
            else:
                UpperCAmelCase_							 =     val[:dim]
                UpperCAmelCase_							 =     val[dim : dim * 2]
                UpperCAmelCase_							 =     val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            UpperCAmelCase_							 =     key.split('''.'''      )
            UpperCAmelCase_							 =     int(key_split[1]      )
            UpperCAmelCase_							 =     config.reduce_dim
            if "weight" in key:
                UpperCAmelCase_							 =     val[:dim, :]
                UpperCAmelCase_							 =     val[dim : dim * 2, :]
                UpperCAmelCase_							 =     val[-dim:, :]
            else:
                UpperCAmelCase_							 =     val[:dim]
                UpperCAmelCase_							 =     val[dim : dim * 2]
                UpperCAmelCase_							 =     val[-dim:]
        else:
            UpperCAmelCase_							 =     rename_key(__UpperCamelCase      )
            if "visual_projection" in new_name or "text_projection" in new_name:
                UpperCAmelCase_							 =     val.T
            UpperCAmelCase_							 =     val

    return orig_state_dict




def 					SCREAMING_SNAKE_CASE	(       )  ->							List[str]:
    UpperCAmelCase_							 =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_							 =     Image.open(requests.get(__UpperCamelCase       ,     stream=__UpperCamelCase      ).raw      )
    return image




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str       ,     __UpperCamelCase			:						Optional[int]       ,     __UpperCamelCase			:						List[str]       ,     __UpperCamelCase			:						Dict      )  ->							Dict:
    UpperCAmelCase_							 =     get_clipseg_config(__UpperCamelCase      )
    UpperCAmelCase_							 =     CLIPSegForImageSegmentation(__UpperCamelCase      )
    model.eval()

    UpperCAmelCase_							 =     torch.load(__UpperCamelCase       ,     map_location='''cpu'''      )

    # remove some keys
    for key in state_dict.copy().keys():
        if key.startswith('''model'''      ):
            state_dict.pop(__UpperCamelCase       ,     __UpperCamelCase      )

    # rename some keys
    UpperCAmelCase_							 =     convert_state_dict(__UpperCamelCase       ,     __UpperCamelCase      )
    UpperCAmelCase_ ,							UpperCAmelCase_							 =     model.load_state_dict(__UpperCamelCase       ,     strict=__UpperCamelCase      )

    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
        raise ValueError('''Missing keys that are not expected: {}'''.format(__UpperCamelCase      )      )
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
        raise ValueError(f'Unexpected keys: {unexpected_keys}'      )

    UpperCAmelCase_							 =     ViTImageProcessor(size=352      )
    UpperCAmelCase_							 =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
    UpperCAmelCase_							 =     CLIPSegProcessor(image_processor=__UpperCamelCase       ,     tokenizer=__UpperCamelCase      )

    UpperCAmelCase_							 =     prepare_img()
    UpperCAmelCase_							 =     ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

    UpperCAmelCase_							 =     processor(text=__UpperCamelCase       ,     images=[image] * len(__UpperCamelCase      )       ,     padding='''max_length'''       ,     return_tensors='''pt'''      )

    with torch.no_grad():
        UpperCAmelCase_							 =     model(**__UpperCamelCase      )

    # verify values
    UpperCAmelCase_							 =     torch.tensor([0.1_110, -0.1_882, 0.1_645]      )
    UpperCAmelCase_							 =     torch.tensor([0.2_692, -0.7_197, -0.1_328]      )
    if model_name == "clipseg-rd64-refined":
        UpperCAmelCase_							 =     torch.tensor(
            [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]      )
    elif model_name == "clipseg-rd64":
        UpperCAmelCase_							 =     torch.tensor(
            [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]      )
    elif model_name == "clipseg-rd16":
        UpperCAmelCase_							 =     torch.tensor(
            [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]      )
    else:
        raise ValueError(f'Model name {model_name} not supported.'      )

    assert torch.allclose(outputs.logits[0, :3, :3]       ,     __UpperCamelCase       ,     atol=1e-3      )
    assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,     __UpperCamelCase       ,     atol=1e-3      )
    assert torch.allclose(outputs.pooled_output[0, :3]       ,     __UpperCamelCase       ,     atol=1e-3      )
    print('''Looks ok!'''      )

    if pytorch_dump_folder_path is not None:
        print(f'Saving model and processor to {pytorch_dump_folder_path}'      )
        model.save_pretrained(__UpperCamelCase      )
        processor.save_pretrained(__UpperCamelCase      )

    if push_to_hub:
        print(f'Pushing model and processor for {model_name} to the hub'      )
        model.push_to_hub(f'CIDAS/{model_name}'      )
        processor.push_to_hub(f'CIDAS/{model_name}'      )


if __name__ == "__main__":
 _lowerCamelCase			 =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='clipseg-rd64',
     type=str,
     choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
     help=(
         'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
         ' reduce dimension)'
     ),
 )
 parser.add_argument(
     '--checkpoint_path',
     default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
     type=str,
     help=(
         'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
         ' the decoder weights.'
     ),
 )
 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.'
 )

 _lowerCamelCase			 =						parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_lowerCamelCase			 =						logging.get_logger(__name__)

_lowerCamelCase			 =						{
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'blenderbot'
     lowerCAmelCase				:       int   		=  ['past_key_values']
     lowerCAmelCase				:       int   		=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
     def __init__(	self   :			int					,    __snake_case   :			Optional[Any]=80_08					,    __snake_case   :			List[str]=1_28					,    __snake_case   :			List[str]=2					,    __snake_case   :			Tuple=1_02_40					,    __snake_case   :			List[Any]=32					,    __snake_case   :			int=24					,    __snake_case   :			str=1_02_40					,    __snake_case   :			Optional[int]=32					,    __snake_case   :			Dict=0.0					,    __snake_case   :			int=0.0					,    __snake_case   :			str=True					,    __snake_case   :			Dict=True					,    __snake_case   :			Dict="gelu"					,    __snake_case   :			List[str]=25_60					,    __snake_case   :			List[Any]=0.1					,    __snake_case   :			str=0.0					,    __snake_case   :			Dict=0.0					,    __snake_case   :			Tuple=0.02					,    __snake_case   :			Any=1					,    __snake_case   :			int=False					,    __snake_case   :			Any=0					,    __snake_case   :			Optional[Any]=1					,    __snake_case   :			Optional[int]=2					,    __snake_case   :			List[Any]=3					,    __snake_case   :			int=2					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     vocab_size
         UpperCAmelCase_							 =     max_position_embeddings
         UpperCAmelCase_							 =     d_model
         UpperCAmelCase_							 =     encoder_ffn_dim
         UpperCAmelCase_							 =     encoder_layers
         UpperCAmelCase_							 =     encoder_attention_heads
         UpperCAmelCase_							 =     decoder_ffn_dim
         UpperCAmelCase_							 =     decoder_layers
         UpperCAmelCase_							 =     decoder_attention_heads
         UpperCAmelCase_							 =     dropout
         UpperCAmelCase_							 =     attention_dropout
         UpperCAmelCase_							 =     activation_dropout
         UpperCAmelCase_							 =     activation_function
         UpperCAmelCase_							 =     init_std
         UpperCAmelCase_							 =     encoder_layerdrop
         UpperCAmelCase_							 =     decoder_layerdrop
         UpperCAmelCase_							 =     use_cache
         UpperCAmelCase_							 =     encoder_layers
         UpperCAmelCase_							 =     scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=__snake_case					,    bos_token_id=__snake_case					,    eos_token_id=__snake_case					,    is_encoder_decoder=__snake_case					,    decoder_start_token_id=__snake_case					,    encoder_no_repeat_ngram_size=__snake_case					,    forced_eos_token_id=__snake_case					,    **__snake_case					,    )



class 			a     (   _A			):

     '''simple docstring'''





     @property
     def   lowerCamelCase_   (	self   :			Any				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]				)
             if self.use_past:
                 UpperCAmelCase_							 =     {0: '''batch'''}
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''decoder_sequence'''}
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''decoder_sequence'''}
             if self.use_past:
                 self.fill_with_past_key_values_(__snake_case					,    direction='''inputs'''				)
         elif self.task == "causal-lm":
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]				)
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ]				)

         return common_inputs
     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super().outputs
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				).outputs
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         # Generate decoder inputs
         UpperCAmelCase_							 =     seq_length if not self.use_past else 1
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         UpperCAmelCase_							 =     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_							 =     dict(**__snake_case					,    **__snake_case				)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
             else:
                 import torch
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     common_inputs['''decoder_input_ids'''].shape[1]
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_attention_heads
             UpperCAmelCase_							 =     (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase_							 =     decoder_seq_length
             UpperCAmelCase_							 =     (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCAmelCase_							 =     torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(__snake_case					,    __snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     []
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

             for _ in range(__snake_case				):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                     )				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
             else:
                 import torch
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     seqlen
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_attention_heads
             UpperCAmelCase_							 =     (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase_							 =     common_inputs['''attention_mask'''].dtype
             UpperCAmelCase_							 =     torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(__snake_case					,    __snake_case					,    dtype=__snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     [
                 (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
             ]
         return common_inputs
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    fixed_dimension=OnnxConfig.default_fixed_batch					,    num_token_to_add=0				)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase_							 =     tokenizer.num_special_tokens_to_add(__snake_case				)
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    fixed_dimension=OnnxConfig.default_fixed_sequence					,    num_token_to_add=__snake_case				)

         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_							 =     [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
         UpperCAmelCase_							 =     dict(tokenizer(__snake_case					,    return_tensors=__snake_case				)				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 __snake_case					,    batch_size=__snake_case					,    seq_length=__snake_case					,    is_pair=__snake_case					,    framework=__snake_case				)

         elif self.task == "causal-lm":
             UpperCAmelCase_							 =     self._generate_dummy_inputs_for_causal_lm(
                 __snake_case					,    batch_size=__snake_case					,    seq_length=__snake_case					,    is_pair=__snake_case					,    framework=__snake_case				)
         else:
             UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __snake_case					,    batch_size=__snake_case					,    seq_length=__snake_case					,    is_pair=__snake_case					,    framework=__snake_case				)

         return common_inputs
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Optional[int]					,    __snake_case   :			int					,    __snake_case   :			str					,    __snake_case   :			Optional[int]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super()._flatten_past_key_values_(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				)._flatten_past_key_values_(
                 __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Mapping[str, Mapping[int, str]]					,    __snake_case   :			str				):
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'				)

         UpperCAmelCase_							 =     '''past_key_values''' if direction == '''inputs''' else '''present'''
         UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

         UpperCAmelCase_							 =     '''past_encoder_sequence'''
         UpperCAmelCase_							 =     '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

         for i in range(__snake_case				):
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}





