


'''simple docstring'''




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


_a      :      List[str]       				=      logging.get_logger(__name__)

_a      :      List[str]       				=      {
    "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  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Dict									=  "blenderbot"
   _SCREAMING_SNAKE_CASE     :     int									=  ["past_key_values"]
   _SCREAMING_SNAKE_CASE     :     Tuple									=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
   def __init__(	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			int=8008					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=128					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			List[str]=1_0240					,      SCREAMING_SNAKE_CASE_					:			List[Any]=32					,      SCREAMING_SNAKE_CASE_					:			int=24					,      SCREAMING_SNAKE_CASE_					:			List[Any]=1_0240					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=32					,      SCREAMING_SNAKE_CASE_					:			List[str]=0.0					,      SCREAMING_SNAKE_CASE_					:			Tuple=0.0					,      SCREAMING_SNAKE_CASE_					:			Any=True					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			List[str]=2560					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=0.1					,      SCREAMING_SNAKE_CASE_					:			Dict=0.0					,      SCREAMING_SNAKE_CASE_					:			Any=0.0					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Dict=1					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=False					,      SCREAMING_SNAKE_CASE_					:			int=0					,      SCREAMING_SNAKE_CASE_					:			Tuple=1					,      SCREAMING_SNAKE_CASE_					:			str=2					,      SCREAMING_SNAKE_CASE_					:			List[Any]=3					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=2					,      **SCREAMING_SNAKE_CASE_					:			int					,      )   ->       Optional[int]:
        __snake_case    			=	vocab_size
        __snake_case    			=	max_position_embeddings
        __snake_case    			=	d_model
        __snake_case    			=	encoder_ffn_dim
        __snake_case    			=	encoder_layers
        __snake_case    			=	encoder_attention_heads
        __snake_case    			=	decoder_ffn_dim
        __snake_case    			=	decoder_layers
        __snake_case    			=	decoder_attention_heads
        __snake_case    			=	dropout
        __snake_case    			=	attention_dropout
        __snake_case    			=	activation_dropout
        __snake_case    			=	activation_function
        __snake_case    			=	init_std
        __snake_case    			=	encoder_layerdrop
        __snake_case    			=	decoder_layerdrop
        __snake_case    			=	use_cache
        __snake_case    			=	encoder_layers
        __snake_case    			=	scale_embedding  # scale factor will be sqrt(d_model) if True

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




class  _lowercase   (  __lowercase       ):
   @property
   def a     (	self					:			Optional[int]			)   ->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]			)
             if self.use_past:
                  __snake_case    			=	{0: 'batch'}
                  __snake_case    			=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                  __snake_case    			=	{0: 'batch', 1: 'decoder_sequence'}
                  __snake_case    			=	{0: 'batch', 1: 'decoder_sequence'}
             if self.use_past:
                  self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_					,      direction='inputs'			)
        elif self.task == "causal-lm":
             __snake_case    			=	OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]			)
             if self.use_past:
                  __snake_case			,					__snake_case    			=	self.num_layers
                  for i in range(SCREAMING_SNAKE_CASE_			):
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
        else:
             __snake_case    			=	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 a     (	self					:			Optional[int]			)   ->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	super().outputs
        else:
             __snake_case    			=	super(SCREAMING_SNAKE_CASE_					,      self			).outputs
             if self.use_past:
                  __snake_case			,					__snake_case    			=	self.num_layers
                  for i in range(SCREAMING_SNAKE_CASE_			):
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        # Generate decoder inputs
        __snake_case    			=	seq_length if not self.use_past else 1
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        __snake_case    			=	dict(**SCREAMING_SNAKE_CASE_					,      **SCREAMING_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
             __snake_case			,					__snake_case    			=	common_inputs['input_ids'].shape
             __snake_case    			=	common_inputs['decoder_input_ids'].shape[1]
             __snake_case			,					__snake_case    			=	self.num_attention_heads
             __snake_case    			=	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __snake_case    			=	decoder_seq_length
             __snake_case    			=	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             __snake_case    			=	torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)]					,      dim=1			)
             __snake_case    			=	[]
             __snake_case			,					__snake_case    			=	self.num_layers

             for _ in range(SCREAMING_SNAKE_CASE_			):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                      )			)
        return common_inputs
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_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
             __snake_case			,					__snake_case    			=	common_inputs['input_ids'].shape
             __snake_case    			=	seqlen
             __snake_case			,					__snake_case    			=	self.num_layers
             __snake_case			,					__snake_case    			=	self.num_attention_heads
             __snake_case    			=	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __snake_case    			=	common_inputs['attention_mask'].dtype
             __snake_case    			=	torch.cat(
                 [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      dtype=SCREAMING_SNAKE_CASE_			)]					,      dim=1			)
             __snake_case    			=	[
                 (torch.zeros(SCREAMING_SNAKE_CASE_			), torch.zeros(SCREAMING_SNAKE_CASE_			)) for _ in range(SCREAMING_SNAKE_CASE_			)
             ]
        return common_inputs
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        # 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
        __snake_case    			=	compute_effective_axis_dimension(
            SCREAMING_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
        __snake_case    			=	tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	compute_effective_axis_dimension(
            SCREAMING_SNAKE_CASE_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=SCREAMING_SNAKE_CASE_			)

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

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

        return common_inputs
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Tuple:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        else:
             __snake_case    			=	super(SCREAMING_SNAKE_CASE_					,      self			)._flatten_past_key_values_(
                 SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)





   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Mapping[str, Mapping[int, str]]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Optional[int]:
        if direction not in ["inputs", "outputs"]:
             raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given'			)

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

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

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








'''simple docstring'''




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a      :      int       				=      logging.get_logger(__name__)





def      _a		(lowercase__ :      int	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	MobileNetVaConfig(layer_norm_eps=0.0_01	)

     if "quant" in model_name:
          raise ValueError('Quantized models are not supported.'	)

     __snake_case    			=	re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'       ,      lowercase__	)
     if matches:
          __snake_case    			=	float(matches[1]	)
          __snake_case    			=	int(matches[2]	)

     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	8
          __snake_case    			=	2_1
          __snake_case    			=	'pascal-voc-id2label.json'
     else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          __snake_case    			=	1_0_0_1
          __snake_case    			=	'imagenet-1k-id2label.json'

     __snake_case    			=	'huggingface/label-files'
     __snake_case    			=	json.load(open(hf_hub_download(lowercase__       ,      lowercase__       ,      repo_type='dataset'	)       ,      'r'	)	)

     if config.num_labels == 1_0_0_1:
          __snake_case    			=	{int(lowercase__	) + 1: v for k, v in idalabel.items()}
          __snake_case    			=	'background'
     else:
          __snake_case    			=	{int(lowercase__	): v for k, v in idalabel.items()}

     __snake_case    			=	idalabel
     __snake_case    			=	{v: k for k, v in idalabel.items()}

     return config





def      _a		() -> Dict:






     """simple docstring"""
     __snake_case    			=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case    			=	Image.open(requests.get(lowercase__       ,      stream=lowercase__	).raw	)
     return im





@torch.no_grad()
def      _a		(lowercase__ :      str       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      Optional[Any]       ,      lowercase__ :      Dict=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	get_mobilenet_va_config(lowercase__	)

     # Load 🤗 model
     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	MobileNetVaForSemanticSegmentation(lowercase__	).eval()
     else:
          __snake_case    			=	MobileNetVaForImageClassification(lowercase__	).eval()

     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(lowercase__       ,      lowercase__       ,      lowercase__	)

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     __snake_case    			=	MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}       ,      size={'shortest_edge': config.image_size + 3_2}       ,      )
     __snake_case    			=	image_processor(images=prepare_img()       ,      return_tensors='pt'	)
     __snake_case    			=	model(**lowercase__	)
     __snake_case    			=	outputs.logits

     if model_name.startswith('deeplabv3_'	):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               __snake_case    			=	torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]	)

          else:
               raise ValueError(f'Unknown model name: {model_name}'	)

          assert torch.allclose(logits[0, :3, :3, :3]       ,      lowercase__       ,      atol=1e-4	)
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               __snake_case    			=	torch.tensor([0.01_81, -1.00_15, 0.46_88]	)
          elif model_name == "mobilenet_v2_1.0_224":
               __snake_case    			=	torch.tensor([0.24_45, -1.19_93, 0.19_05]	)
          elif model_name == "mobilenet_v2_0.75_160":
               __snake_case    			=	torch.tensor([0.24_82, 0.41_36, 0.66_69]	)
          elif model_name == "mobilenet_v2_0.35_96":
               __snake_case    			=	torch.tensor([0.14_51, -0.46_24, 0.71_92]	)
          else:
               __snake_case    			=	None

          if expected_logits is not None:
               assert torch.allclose(logits[0, :3]       ,      lowercase__       ,      atol=1e-4	)

     Path(lowercase__	).mkdir(exist_ok=lowercase__	)
     print(f'Saving model {model_name} to {pytorch_dump_folder_path}'	)
     model.save_pretrained(lowercase__	)
     print(f'Saving image processor to {pytorch_dump_folder_path}'	)
     image_processor.save_pretrained(lowercase__	)

     if push_to_hub:
          print('Pushing to the hub...'	)
          __snake_case    			=	'google/' + model_name
          image_processor.push_to_hub(lowercase__	)
          model.push_to_hub(lowercase__	)


if __name__ == "__main__":
       _a      :      List[str]       				=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="mobilenet_v2_1.0_224",
           type=str,
           help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
       )
       parser.add_argument(
           "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, 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      :      str       				=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




