
"""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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			lowerCamelCase_					=  1_001
			lowerCamelCase_					=  '''imagenet-1k-id2label.json'''
			lowerCamelCase_					=  '''huggingface/label-files'''
			lowerCamelCase_					=  json.load(open(hf_hub_download(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						) + 1: v for k, v in idalabel.items()}
			lowerCamelCase_					=  '''background'''
			lowerCamelCase_					=  idalabel
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

			return config






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






@torch.no_grad()
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  get_mobilenet_va_config(lowerCAmelCase__						)

			# Load 🤗 model
			lowerCamelCase_					=  MobileNetVaForImageClassification(lowerCAmelCase__						).eval()

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

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

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

			if model_name == "mobilenet_v1_1.0_224":
						lowerCamelCase_					=  torch.tensor([-4.1_739, -1.1_233, 3.1_205]						)
			elif model_name == "mobilenet_v1_0.75_192":
						lowerCamelCase_					=  torch.tensor([-3.9_440, -2.3_141, -0.3_333]						)
			else:
						lowerCamelCase_					=  None

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

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

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


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"""


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
						import optax
						from flax.training.common_utils import onehot

						from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
						from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



