






'''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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__						:Union[str, Any]          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				MobileNetVaConfig(layer_norm_eps=0.001     )

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

     __lowerCamelCase						:       Dict				   =				re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$""",						_lowercase     )
     if matches:
          __lowerCamelCase						:       int				   =				float(matches[1]     )
          __lowerCamelCase						:       str				   =				int(matches[2]     )

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     __lowerCamelCase						:       Any				   =				1_001
     __lowerCamelCase						:       Union[str, Any]				   =				"""imagenet-1k-id2label.json"""
     __lowerCamelCase						:       Tuple				   =				"""huggingface/label-files"""
     __lowerCamelCase						:       Optional[Any]				   =				json.load(open(hf_hub_download(_lowercase,						_lowercase,						repo_type="""dataset"""     ),						"""r"""     )     )
     __lowerCamelCase						:       Tuple				   =				{int(_lowercase     ) + 1: v for k, v in idalabel.items()}
     __lowerCamelCase						:       List[str]				   =				"""background"""
     __lowerCamelCase						:       str				   =				idalabel
     __lowerCamelCase						:       Dict				   =				{v: k for k, v in idalabel.items()}

     return config
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __lowerCamelCase						:       List[Any]				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     return im
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase=False     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				get_mobilenet_va_config(_lowercase     )

     # Load 🤗 model
     __lowerCamelCase						:       int				   =				MobileNetVaForImageClassification(_lowercase     ).eval()

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

     # Check outputs on an image, prepared by MobileNetV1ImageProcessor
     __lowerCamelCase						:       str				   =				MobileNetVaImageProcessor(
         crop_size={"""width""": config.image_size, """height""": config.image_size},						size={"""shortest_edge""": config.image_size + 32},						)
     __lowerCamelCase						:       Tuple				   =				image_processor(images=prepare_img(),						return_tensors="""pt"""     )
     __lowerCamelCase						:       List[Any]				   =				model(**_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				outputs.logits

     assert logits.shape == (1, 1_001)

     if model_name == "mobilenet_v1_1.0_224":
          __lowerCamelCase						:       Optional[int]				   =				torch.tensor([-4.1739, -1.1233, 3.1205]     )
     elif model_name == "mobilenet_v1_0.75_192":
          __lowerCamelCase						:       List[str]				   =				torch.tensor([-3.9440, -2.3141, -0.3333]     )
     else:
          __lowerCamelCase						:       Tuple				   =				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..."""     )
          __lowerCamelCase						:       Union[str, Any]				   =				"""google/""" + model_name
          image_processor.push_to_hub(_lowercase     )
          model.push_to_hub(_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Optional[Any]          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""mobilenet_v1_1.0_224""",
       type=str,
       help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."""
   )

   UpperCAmelCase__						:str          =			parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )











'''simple docstring'''

from ...processing_utils import ProcessorMixin







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[Any]   		=			'SpeechT5FeatureExtractor'
       snake_case__    :     Union[str, Any]   		=			'SpeechT5Tokenizer'




       def __init__(					self      : Union[str, Any]	,    A__      : str	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__(A__	,    A__					)




       def __call__(					self      : List[Any]	,    *A__      : Any	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				kwargs.pop("""audio"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""text"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""text_target"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""audio_target"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""sampling_rate"""	,    A__					)

            if audio is not None and text is not None:
                 raise ValueError(
                     """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""					)
            if audio_target is not None and text_target is not None:
                 raise ValueError(
                     """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""					)
            if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""					)

            if audio is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(A__	,    *A__	,    sampling_rate=A__	,    **A__					)
            elif text is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.tokenizer(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Tuple				   =				None

            if audio_target is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(audio_target=A__	,    *A__	,    sampling_rate=A__	,    **A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				targets["""input_values"""]
            elif text_target is not None:
                 __lowerCamelCase						:       Tuple				   =				self.tokenizer(A__	,    **A__					)
                 __lowerCamelCase						:       List[str]				   =				targets["""input_ids"""]
            else:
                 __lowerCamelCase						:       Dict				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       str				   =				labels

                 __lowerCamelCase						:       int				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       Optional[int]				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Any	,    *A__      : Union[str, Any]	,    **A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""input_values"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""input_ids"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""labels"""	,    A__					)

            if input_values is not None and input_ids is not None:
                 raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""					)
            if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""					)

            if input_values is not None:
                 __lowerCamelCase						:       List[Any]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
            elif input_ids is not None:
                 __lowerCamelCase						:       Any				   =				self.tokenizer.pad(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Any				   =				None

            if labels is not None:
                 if "input_ids" in labels or (isinstance(A__	,    A__					) and "input_ids" in labels[0]):
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer.pad(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				targets["""input_ids"""]
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor.feature_size
                      __lowerCamelCase						:       Any				   =				self.feature_extractor.num_mel_bins
                      __lowerCamelCase						:       List[str]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				feature_size_hack
                      __lowerCamelCase						:       List[Any]				   =				targets["""input_values"""]
            else:
                 __lowerCamelCase						:       Optional[int]				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       Dict				   =				labels

                 __lowerCamelCase						:       List[str]				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       int				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Optional[Any]	,    *A__      : List[Any]	,    **A__      : Optional[int]					):


            """simple docstring"""







            return self.tokenizer.batch_decode(*A__	,    **A__					)




       def 					a_					(					self      : Any	,    *A__      : int	,    **A__      : Dict					):


            """simple docstring"""







            return self.tokenizer.decode(*A__	,    **A__					)



