
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()
_lowerCamelCase        =logging.get_logger(__name__)





def    _a (						lowerCamelCase							):
	lowerCamelCase		:     int          =				MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

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

	lowerCamelCase		:     List[str]          =				re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$""",     lowerCamelCase							)
	if matches:
		lowerCamelCase		:     Tuple          =				float(matches[1]							)
		lowerCamelCase		:     Union[str, Any]          =				int(matches[2]							)

	if model_name.startswith("""deeplabv3_"""							):
		lowerCamelCase		:     List[Any]          =				8
		lowerCamelCase		:     Any          =				21
		lowerCamelCase		:     Optional[Any]          =				"""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".
		lowerCamelCase		:     Tuple          =				1001
		lowerCamelCase		:     Any          =				"""imagenet-1k-id2label.json"""

	lowerCamelCase		:     Any          =				"""huggingface/label-files"""
	lowerCamelCase		:     int          =				json.load(open(hf_hub_download(lowerCamelCase,     lowerCamelCase,     repo_type="""dataset"""							),     """r"""							)							)

	if config.num_labels == 1001:
		lowerCamelCase		:     Dict          =				{int(lowerCamelCase							) + 1: v for k, v in idalabel.items()}
		lowerCamelCase		:     int          =				"""background"""
	else:
		lowerCamelCase		:     Union[str, Any]          =				{int(lowerCamelCase							): v for k, v in idalabel.items()}

	lowerCamelCase		:     Union[str, Any]          =				idalabel
	lowerCamelCase		:     List[str]          =				{v: k for k, v in idalabel.items()}

	return config





def    _a (						):
	lowerCamelCase		:     Union[str, Any]          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	lowerCamelCase		:     List[str]          =				Image.open(requests.get(lowerCamelCase,     stream=lowerCamelCase							).raw							)
	return im





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase=False							):
	lowerCamelCase		:     str          =				get_mobilenet_va_config(lowerCamelCase							)

	# Load 🤗 model
	if model_name.startswith("""deeplabv3_"""							):
		lowerCamelCase		:     Tuple          =				MobileNetVaForSemanticSegmentation(lowerCamelCase							).eval()
	else:
		lowerCamelCase		:     Any          =				MobileNetVaForImageClassification(lowerCamelCase							).eval()

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

	# Check outputs on an image, prepared by MobileNetV2ImageProcessor
	lowerCamelCase		:     Optional[int]          =				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		:     Optional[int]          =				model(**lowerCamelCase							)
	lowerCamelCase		:     List[Any]          =				outputs.logits

	if model_name.startswith("""deeplabv3_"""							):
		assert logits.shape == (1, 21, 65, 65)

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			lowerCamelCase		:     Optional[Any]          =				torch.tensor(
			    [
			        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
			        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
			        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
			    ]							)

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

		assert torch.allclose(logits[0, :3, :3, :3],     lowerCamelCase,     atol=1e-4							)
	else:
		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v2_1.4_224":
			lowerCamelCase		:     Any          =				torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]							)
		elif model_name == "mobilenet_v2_1.0_224":
			lowerCamelCase		:     Union[str, Any]          =				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]							)
		elif model_name == "mobilenet_v2_0.75_160":
			lowerCamelCase		:     str          =				torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]							)
		elif model_name == "mobilenet_v2_0.35_96":
			lowerCamelCase		:     List[Any]          =				torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]							)
		else:
			lowerCamelCase		:     Union[str, Any]          =				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		:     List[Any]          =				"""google/""" + model_name
		image_processor.push_to_hub(lowerCamelCase							)
		model.push_to_hub(lowerCamelCase							)


if __name__ == "__main__":
						_lowerCamelCase        =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."""
						)

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






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




