



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()
__snake_case						:List[Any]						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    MobileNetVaConfig(layer_norm_eps=0.0_01    )

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

    __a							=    re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''       ,			_UpperCAmelCase    )
    if matches:
        __a							=    float(matches[1]    )
        __a							=    int(matches[2]    )

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    __a							=    1001
    __a							=    '''imagenet-1k-id2label.json'''
    __a							=    '''huggingface/label-files'''
    __a							=    json.load(open(hf_hub_download(_UpperCAmelCase       ,			_UpperCAmelCase       ,			repo_type='''dataset'''    )       ,			'''r'''    )    )
    __a							=    {int(_UpperCAmelCase    ) + 1: v for k, v in idalabel.items()}
    __a							=    '''background'''
    __a							=    idalabel
    __a							=    {v: k for k, v in idalabel.items()}

    return config





def 					__snake_case     (      ):
    __a							=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a							=    Image.open(requests.get(_UpperCAmelCase       ,			stream=_UpperCAmelCase    ).raw    )
    return im





@torch.no_grad()
def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=False    ):
    __a							=    get_mobilenet_va_config(_UpperCAmelCase    )

    # Load 🤗 model
    __a							=    MobileNetVaForImageClassification(_UpperCAmelCase    ).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    __a							=    MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}       ,			size={'''shortest_edge''': config.image_size + 32}       ,			)
    __a							=    image_processor(images=prepare_img()       ,			return_tensors='''pt'''    )
    __a							=    model(**_UpperCAmelCase    )
    __a							=    outputs.logits

    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v1_1.0_224":
        __a							=    torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
    elif model_name == "mobilenet_v1_0.75_192":
        __a							=    torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
    else:
        __a							=    None

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

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

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


if __name__ == "__main__":
 __snake_case						:Union[str, 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.'''
 )

 __snake_case						:Union[str, Any]						=					parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__snake_case						:Dict						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		*__SCREAMING_SNAKE_CASE							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           requires_backends(self					,		'''vision''')
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __a							=    {}
           __a							=    {}

           if prompt is not None:
               __a							=    prompt

           if generate_kwargs is not None:
               __a							=    generate_kwargs
           if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                   __a							=    {}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                       ''' please use only one''')
               __a							=    max_new_tokens
           return preprocess_params, forward_kwargs, {}



       def __call__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image.Image", List["Image.Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None):


           '''simple docstring'''

           __a							=    load_image(__SCREAMING_SNAKE_CASE)

           if prompt is not None:
               if not isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. '
                       '''Note also that one single text can be provided for conditional image to text generation.''')

               __a							=    self.model.config.model_type

               if model_type == "git":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(text=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
                   __a							=    [self.tokenizer.cls_token_id] + input_ids
                   __a							=    torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
                   model_inputs.update({'''input_ids''': input_ids})

               elif model_type == "pix2struct":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		header_text=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

               elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   model_inputs.update(__SCREAMING_SNAKE_CASE)

               else:
                   raise ValueError(F'Model type {model_type} does not support conditional text generation')

           else:
               __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
               __a							=    None

           return model_inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']					,		__SCREAMING_SNAKE_CASE)
               and all(x is None for x in model_inputs['''input_ids'''])
           ):
               __a							=    None

           if generate_kwargs is None:
               __a							=    {}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           __a							=    model_inputs.pop(self.model.main_input_name)
           __a							=    self.model.generate(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    []
           for output_ids in model_outputs:
               __a							=    {
                   '''generated_text''': self.tokenizer.decode(
                       __SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE					,		)
               }
               records.append(__SCREAMING_SNAKE_CASE)
           return records





