




"""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()
a_        =       logging.get_logger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Dict								=       MobileNetVaConfig(layer_norm_eps=0.001	)

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

			__lowercase			:  str								=       re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''	,				__UpperCamelCase	)
			if matches:
						__lowercase			:  Any								=       float(matches[1]	)
						__lowercase			:  Any								=       int(matches[2]	)

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__lowercase			:  Optional[int]								=       10_01
			__lowercase			:  List[str]								=       '''imagenet-1k-id2label.json'''
			__lowercase			:  List[str]								=       '''huggingface/label-files'''
			__lowercase			:  str								=       json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type='''dataset'''	)	,				'''r'''	)	)
			__lowercase			:  List[str]								=       {int(__UpperCamelCase	) + 1: v for k, v in idalabel.items()}
			__lowercase			:  str								=       '''background'''
			__lowercase			:  Dict								=       idalabel
			__lowercase			:  List[str]								=       {v: k for k, v in idalabel.items()}

			return config







def      __UpperCAmelCase     ( ):
			__lowercase			:  Optional[int]								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase			:  Tuple								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	)
			return im







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False	):
			__lowercase			:  List[Any]								=       get_mobilenet_va_config(__UpperCamelCase	)

			# Load 🤗 model
			__lowercase			:  Dict								=       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
			__lowercase			:  Optional[Any]								=       MobileNetVaImageProcessor(
			    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,				size={'''shortest_edge''': config.image_size + 32}	,				)
			__lowercase			:  Tuple								=       image_processor(images=prepare_img()	,				return_tensors='''pt'''	)
			__lowercase			:  Union[str, Any]								=       model(**__UpperCamelCase	)
			__lowercase			:  Dict								=       outputs.logits

			assert logits.shape == (1, 10_01)

			if model_name == "mobilenet_v1_1.0_224":
						__lowercase			:  str								=       torch.tensor([-4.1_739, -1.1_233, 3.1_205]	)
			elif model_name == "mobilenet_v1_0.75_192":
						__lowercase			:  List[Any]								=       torch.tensor([-3.9_440, -2.3_141, -0.3_333]	)
			else:
						__lowercase			:  Optional[int]								=       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...'''	)
						__lowercase			:  Optional[int]								=       '''google/''' + model_name
						image_processor.push_to_hub(__UpperCamelCase	)
						model.push_to_hub(__UpperCamelCase	)


if __name__ == "__main__":
			a_        =       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.'
			)

			a_        =       parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)








"""simple docstring"""







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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "perceiver"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=1280						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=26						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__="kv"						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=262						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=56						,	SCREAMING_SNAKE_CASE__=[368, 496]						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=1920						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=[1, 16, 224, 224]						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

				A__											=					num_latents
				A__											=					d_latents
				A__											=					d_model
				A__											=					num_blocks
				A__											=					num_self_attends_per_block
				A__											=					num_self_attention_heads
				A__											=					num_cross_attention_heads
				A__											=					qk_channels
				A__											=					v_channels
				A__											=					cross_attention_shape_for_attention
				A__											=					self_attention_widening_factor
				A__											=					cross_attention_widening_factor
				A__											=					hidden_act
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					layer_norm_eps
				A__											=					use_query_residual
				# masked language modeling attributes
				A__											=					vocab_size
				A__											=					max_position_embeddings
				# image classification attributes
				A__											=					image_size
				# flow attributes
				A__											=					train_size
				# multimodal autoencoding attributes
				A__											=					num_frames
				A__											=					audio_samples_per_frame
				A__											=					samples_per_patch
				A__											=					output_shape






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					A__											=					{0: "batch", 1: "choice", 2: "sequence"}
				else:
					A__											=					{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("inputs", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-4







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					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
					A__											=					preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
					A__											=					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
					A__											=					[" ".join(["a"]		) * seq_length] * batch_size
					A__											=					dict(preprocessor(SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("input_ids"		)
					return inputs
				elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) and preprocessor.model_input_names[0] == "pixel_values":
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch		)
					A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					A__											=					dict(preprocessor(images=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("pixel_values"		)
					return inputs
				else:
					raise ValueError(
					    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)




