

import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

lowerCAmelCase       :				Optional[Any]         =					logging.getLogger(__name__)
@require_tensorboard
class 	_A						(					unittest.TestCase):

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					str          =       'test_project_with_config'
								with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       Accelerator(log_with='tensorboard'	,   project_dir=snake_case__   )
														SCREAMING_SNAKE_CASE_			:					int          =       {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(snake_case__	,   snake_case__   )
														accelerator.end_training()
														for child in Path(f"{dirpath}/{project_name}"   ).glob('*/**'   ):
																				SCREAMING_SNAKE_CASE_			:					int          =       list(filter(lambda _SCREAMING_SNAKE_CASE   : x.is_file()	,   child.iterdir()   )   )[0]
														self.assertNotEqual(str(snake_case__   )	,   ''   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					List[Any]          =       'test_project_with_log'
								with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       Accelerator(log_with='tensorboard'	,   project_dir=snake_case__   )
														accelerator.init_trackers(snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Any          =       {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(snake_case__	,   step=0   )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE_			:					str          =       list(filter(lambda _SCREAMING_SNAKE_CASE   : x.is_file()	,   Path(f"{dirpath}/{project_name}"   ).iterdir()   )   )[0]
														self.assertNotEqual(str(snake_case__   )	,   ''   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       'test_project_with_log'
								with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       Accelerator(log_with='tensorboard'	,   project_dir=snake_case__   )
														accelerator.init_trackers(snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Tuple          =       {'tensor': torch.tensor(1   )}
														accelerator.log(snake_case__	,   step=0   )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       list(filter(lambda _SCREAMING_SNAKE_CASE   : x.is_file()	,   Path(f"{dirpath}/{project_name}"   ).iterdir()   )   )[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(snake_case__	,   'rb'   ) as f:
																				SCREAMING_SNAKE_CASE_			:					Tuple          =       f.read()
														SCREAMING_SNAKE_CASE_			:					Tuple          =       False
														while data:
																				SCREAMING_SNAKE_CASE_			:					List[str]          =       struct.unpack('Q'	,   data[:8]   )

																				SCREAMING_SNAKE_CASE_			:					int          =       data[12 : 12 + int(header[0]   )]  # 8+4
																				SCREAMING_SNAKE_CASE_			:					Dict          =       data[12 + int(header[0]   ) + 4 :]
																				SCREAMING_SNAKE_CASE_			:					Dict          =       event_pba.Event()

																				event.ParseFromString(snake_case__   )
																				if event.HasField('summary'   ):
																										for value in event.summary.value:
																																if value.simple_value == 1.0 and value.tag == "tensor":
																																						SCREAMING_SNAKE_CASE_			:					List[str]          =       True
														self.assertTrue(snake_case__	,   'Converted tensor was not found in the log file!'   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								with self.assertRaisesRegex(snake_case__	,   'Logging with `tensorboard` requires a `logging_dir`'   ):
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       Accelerator(log_with='tensorboard'   )
								with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       Accelerator(log_with='tensorboard'	,   project_dir=snake_case__   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Tuple          =       ProjectConfiguration(total_limit=30   )
								with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       Accelerator(log_with='tensorboard'	,   project_dir=snake_case__	,   project_config=snake_case__   )

@require_wandb
@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''})
class 	_A						(					a_				,       a_):

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								super().setUp()
								# wandb let's us override where logs are stored to via the WANDB_DIR env var
								self.add_mocks(mock.patch.dict(os.environ	,   {'WANDB_DIR': self.tmpdir}   )   )

		@staticmethod
		def 	UpperCAmelCase      (			_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = True   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       rf"{section} ([\S\s]*?)\n\n"
								if record:
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       rf"Record: {pattern}"
								SCREAMING_SNAKE_CASE_			:					int          =       re.findall(snake_case__	,   snake_case__   )[0]
								# A config
								if section == "config" or section == "history":
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       re.findall(r'\"([a-zA-Z0-9_.,]+)'	,   snake_case__   )
														return {key: val for key, val in zip(cleaned_record[0::2]	,   cleaned_record[1::2]   )}
								# Everything else
								else:
														return dict(re.findall(r'(\w+): \"([^\s]+)\"'	,   snake_case__   )   )



		@skip
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       'test_project_with_config'
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       Accelerator(log_with='wandb'   )
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
								SCREAMING_SNAKE_CASE_			:					int          =       {'wandb': {'tags': ['my_tag']}}
								accelerator.init_trackers(snake_case__	,   snake_case__	,   snake_case__   )
								SCREAMING_SNAKE_CASE_			:					Any          =       {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
								accelerator.log(snake_case__	,   step=0   )
								accelerator.end_training()
								# The latest offline log is stored at wandb/latest-run/*.wandb
								for child in Path(f"{self.tmpdir}/wandb/latest-run"   ).glob('*'   ):
														if child.is_file() and child.suffix == ".wandb":
																				SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       subprocess.check_output(
																				    ['wandb', 'sync', '--view', '--verbose', str(snake_case__   )]	,   env=os.environ.copy()   ).decode('utf8'	,   'ignore'   )
																				break

        # Check HPS through careful parsing and cleaning
								SCREAMING_SNAKE_CASE_			:					str          =       self.parse_log(snake_case__	,   'config'   )
								self.assertEqual(logged_items['num_iterations']	,   '12'   )
								self.assertEqual(logged_items['learning_rate']	,   '0.01'   )
								self.assertEqual(logged_items['some_boolean']	,   'false'   )
								self.assertEqual(logged_items['some_string']	,   'some_value'   )
								self.assertEqual(logged_items['some_string']	,   'some_value'   )

								# Run tags
								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.parse_log(snake_case__	,   'run'	,   snake_case__   )
								self.assertEqual(logged_items['tags']	,   'my_tag'   )

								# Actual logging
								SCREAMING_SNAKE_CASE_			:					Any          =       self.parse_log(snake_case__	,   'history'   )
								self.assertEqual(logged_items['total_loss']	,   '0.1'   )
								self.assertEqual(logged_items['iteration']	,   '1'   )
								self.assertEqual(logged_items['my_text']	,   'some_value'   )
								self.assertEqual(logged_items['_step']	,   '0'   )

def 				A_					(							self   ,				a   ,				a					):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       run_name
						SCREAMING_SNAKE_CASE_			:					List[str]          =       OfflineExperiment(project_name=_lowerCAmelCase   ,				offline_directory=_lowerCAmelCase					)
						logger.info(f"Initialized offline CometML project {self.run_name}"					)
						logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'					)
@require_comet_ml
@mock.patch.object(a_				,       '''__init__'''				,       a_)
class 	_A						(					unittest.TestCase):

		@staticmethod
		def 	UpperCAmelCase      (			_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = False   ):





								"""simple docstring"""







								for log in log_list:
														SCREAMING_SNAKE_CASE_			:					int          =       json.loads(snake_case__   )['payload']
														if is_param and "param" in j.keys():
																				if j["param"]["paramName"] == key:
																										return j["param"]["paramValue"]
														if "log_other" in j.keys():
																				if j["log_other"]["key"] == key:
																										return j["log_other"]["val"]
														if "metric" in j.keys():
																				if j["metric"]["metricName"] == key:
																										return j["metric"]["metricValue"]

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE_			:					Tuple          =       CometMLTracker('test_project_with_config'	,   snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Tuple          =       Accelerator(log_with=snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(snake_case__	,   snake_case__   )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       os.listdir(snake_case__   )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       os.path.join(snake_case__	,   snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Dict          =       zipfile.ZipFile(snake_case__	,   'r'   )
														SCREAMING_SNAKE_CASE_			:					Tuple          =       archive.open('messages.json'   ).read().decode('utf-8'   )
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       log.split('\n'   )[:-1]
								self.assertEqual(self.get_value_from_key(snake_case__	,   'num_iterations'	,   snake_case__   )	,   12   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'learning_rate'	,   snake_case__   )	,   0.01   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'some_boolean'	,   snake_case__   )	,   snake_case__   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'some_string'	,   snake_case__   )	,   'some_value'   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE_			:					Tuple          =       CometMLTracker('test_project_with_config'	,   snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       Accelerator(log_with=snake_case__   )
														accelerator.init_trackers(snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Any          =       {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(snake_case__	,   step=0   )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE_			:					List[str]          =       os.listdir(snake_case__   )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE_			:					Any          =       os.path.join(snake_case__	,   snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       zipfile.ZipFile(snake_case__	,   'r'   )
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       archive.open('messages.json'   ).read().decode('utf-8'   )
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       log.split('\n'   )[:-1]
								self.assertEqual(self.get_value_from_key(snake_case__	,   'curr_step'	,   snake_case__   )	,   0   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'total_loss'   )	,   0.1   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'iteration'   )	,   1   )
								self.assertEqual(self.get_value_from_key(snake_case__	,   'my_text'   )	,   'some_value'   )

class 	_A						(					a_):
		SCREAMING_SNAKE_CASE      : int    =					[
		    '''total_loss''',
		    '''iteration''',
		    '''my_text''',
		    '''learning_rate''',
		    '''num_iterations''',
		    '''some_boolean''',
		    '''some_string''',
		]

		SCREAMING_SNAKE_CASE      : Optional[int]    =					'''my_custom_tracker'''
		SCREAMING_SNAKE_CASE      : str    =					False

		def __init__(			self	,   _SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       open(f"{dir}/log.csv"	,   'w+'   )
								SCREAMING_SNAKE_CASE_			:					List[str]          =       csv.DictWriter(self.f	,   fieldnames=self._col_names   )
								self.writer.writeheader()

		@property
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								return self.writer

		def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								logger.info('Call init'   )
								self.writer.writerow(snake_case__   )

		def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								logger.info('Call log'   )
								self.writer.writerow(snake_case__   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								self.f.close()

class 	_A						(					unittest.TestCase):

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE_			:					int          =       MyCustomTracker(snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Dict          =       Accelerator(log_with=snake_case__   )
														SCREAMING_SNAKE_CASE_			:					int          =       {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers('Some name'	,   snake_case__   )
														accelerator.end_training()
														with open(f"{d}/log.csv"	,   'r'   ) as f:
																				SCREAMING_SNAKE_CASE_			:					str          =       csv.DictReader(snake_case__   )
																				SCREAMING_SNAKE_CASE_			:					Optional[int]          =       next(snake_case__   )
																				SCREAMING_SNAKE_CASE_			:					List[Any]          =       {
																				    'total_loss': '',
																				    'iteration': '',
																				    'my_text': '',
																				    'learning_rate': '0.01',
																				    'num_iterations': '12',
																				    'some_boolean': 'False',
																				    'some_string': 'some_value',
																				}
																				self.assertDictEqual(snake_case__	,   snake_case__   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE_			:					str          =       MyCustomTracker(snake_case__   )
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       Accelerator(log_with=snake_case__   )
														accelerator.init_trackers('Some name'   )
														SCREAMING_SNAKE_CASE_			:					Dict          =       {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(snake_case__	,   step=0   )
														accelerator.end_training()
														with open(f"{d}/log.csv"	,   'r'   ) as f:
																				SCREAMING_SNAKE_CASE_			:					List[Any]          =       csv.DictReader(snake_case__   )
																				SCREAMING_SNAKE_CASE_			:					int          =       next(snake_case__   )
																				SCREAMING_SNAKE_CASE_			:					List[Any]          =       {
																				    'total_loss': '0.1',
																				    'iteration': '1',
																				    'my_text': 'some_value',
																				    'learning_rate': '',
																				    'num_iterations': '',
																				    'some_boolean': '',
																				    'some_string': '',
																				}
																				self.assertDictEqual(snake_case__	,   snake_case__   )









import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       :				str         =					logging.get_logger(__name__)

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					str          =       tf.image.resize(a   ,				(3_4_6, 3_4_6)					)
      SCREAMING_SNAKE_CASE_			:					Any          =       tf.image.crop_to_bounding_box(a   ,				(3_4_6 - 2_8_9) // 2   ,				(3_4_6 - 2_8_9) // 2   ,				2_8_9   ,				2_8_9					)
      return image

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       EfficientNetConfig.from_pretrained('google/efficientnet-b7'					)
      SCREAMING_SNAKE_CASE_			:					int          =       2_8_9
      SCREAMING_SNAKE_CASE_			:					int          =       6_4_0
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       {'0': 'LABEL_0', '1': 'LABEL_1'}
      SCREAMING_SNAKE_CASE_			:					Tuple          =       {'LABEL_0': 0, 'LABEL_1': 1}
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       []

      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       BertConfig()
      SCREAMING_SNAKE_CASE_			:					Dict          =       AlignConfig.from_text_vision_configs(
          text_config=a   ,				vision_config=a   ,				projection_dim=6_4_0					)
      return config

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Dict          =       'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       Image.open(requests.get(a   ,				stream=a					).raw					)
      return im

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       EfficientNetImageProcessor(
          do_center_crop=a   ,				rescale_factor=1 / 1_27.5   ,				rescale_offset=a   ,				do_normalize=a   ,				include_top=a   ,				resample=Image.BILINEAR   ,				)
      SCREAMING_SNAKE_CASE_			:					List[str]          =       BertTokenizer.from_pretrained('bert-base-uncased'					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       6_4
      SCREAMING_SNAKE_CASE_			:					Tuple          =       AlignProcessor(image_processor=a   ,				tokenizer=a					)
      return processor

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       [v.split('_'					)[0].split('block'					)[1] for v in original_param_names if v.startswith('block'					)]
      SCREAMING_SNAKE_CASE_			:					Any          =       list(set(a					)					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       sorted(a					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					int          =       {b: str(a					) for b, i in zip(a   ,				range(a					)					)}

      SCREAMING_SNAKE_CASE_			:					str          =       []
      rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')					)
      rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')					)
      rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')					)
      rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')					)
      rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')					)

      for b in block_names:
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       block_name_mapping[b]
            rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")					)
            rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")					)
            rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")					)
            rename_keys.append(
                (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")					)
            rename_keys.append(
                (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")					)
            rename_keys.append(
                (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")					)
            rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")					)
            rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")					)
            rename_keys.append(
                (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")					)
            rename_keys.append(
                (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")					)

            rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")					)
            rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")					)
            rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")					)
            rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")					)
            rename_keys.append(
                (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")					)
            rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")					)
            rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")					)
            rename_keys.append(
                (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")					)
            rename_keys.append(
                (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")					)

      SCREAMING_SNAKE_CASE_			:					List[str]          =       {}
      for item in rename_keys:
            if item[0] in original_param_names:
                  SCREAMING_SNAKE_CASE_			:					Any          =       'vision_model.' + item[1]

    # BERT text encoder
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       []
      SCREAMING_SNAKE_CASE_			:					Tuple          =       'tf_bert_model/bert'
      SCREAMING_SNAKE_CASE_			:					Any          =       'text_model'
      for i in range(1_2					):
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.query.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.query.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.key.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.key.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.self.value.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
                    f"{new}.encoder.layer.{i}.attention.self.value.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
                    f"{new}.encoder.layer.{i}.attention.output.dense.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
                    f"{new}.encoder.layer.{i}.attention.output.dense.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
                    f"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
                    f"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
                    f"{new}.encoder.layer.{i}.intermediate.dense.weight",
                )					)
            rename_keys.append(
                (
                    f"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
                    f"{new}.encoder.layer.{i}.intermediate.dense.bias",
                )					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/dense/kernel:0", f"{new}.encoder.layer.{i}.output.dense.weight")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/dense/bias:0", f"{new}.encoder.layer.{i}.output.dense.bias")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", f"{new}.encoder.layer.{i}.output.LayerNorm.weight")					)
            rename_keys.append(
                (f"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", f"{new}.encoder.layer.{i}.output.LayerNorm.bias")					)

      rename_keys.append((f"{old}/embeddings/word_embeddings/weight:0", f"{new}.embeddings.word_embeddings.weight")					)
      rename_keys.append(
          (f"{old}/embeddings/position_embeddings/embeddings:0", f"{new}.embeddings.position_embeddings.weight")					)
      rename_keys.append(
          (f"{old}/embeddings/token_type_embeddings/embeddings:0", f"{new}.embeddings.token_type_embeddings.weight")					)
      rename_keys.append((f"{old}/embeddings/LayerNorm/gamma:0", f"{new}.embeddings.LayerNorm.weight")					)
      rename_keys.append((f"{old}/embeddings/LayerNorm/beta:0", f"{new}.embeddings.LayerNorm.bias")					)

      rename_keys.append((f"{old}/pooler/dense/kernel:0", f"{new}.pooler.dense.weight")					)
      rename_keys.append((f"{old}/pooler/dense/bias:0", f"{new}.pooler.dense.bias")					)
      rename_keys.append(('dense/kernel:0', 'text_projection.weight')					)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')					)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')					)
      rename_keys.append(('temperature:0', 'temperature')					)

      for item in rename_keys:
            if item[0] in original_param_names:
                  SCREAMING_SNAKE_CASE_			:					int          =       item[1]
      return key_mapping

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      list(hf_params.keys()					)

      for key, value in tf_params.items():
            if key not in key_mapping:
                  continue

            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					str          =       torch.from_numpy(a					).permute(3   ,				2   ,				0   ,				1					)
            elif "embeddings" in key:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.from_numpy(a					)
            elif "depthwise_kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					Dict          =       torch.from_numpy(a					).permute(2   ,				3   ,				0   ,				1					)
            elif "kernel" in key:
                  SCREAMING_SNAKE_CASE_			:					Any          =       torch.from_numpy(np.transpose(a					)					)
            elif "temperature" in key:
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       value
            elif "bn/gamma" or "bn/beta" in key:
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.from_numpy(np.transpose(a					)					).squeeze()
            else:
                  SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.from_numpy(a					)

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(a					)

@torch.no_grad()
def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       6_4
      SCREAMING_SNAKE_CASE_			:					List[str]          =       Tokenizer(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       align.Align('efficientnet-b7'   ,				'bert-base'   ,				6_4_0   ,				a   ,				tok.get_vocab_size()					)
      original_model.compile()
      original_model.load_weights(a					)

      SCREAMING_SNAKE_CASE_			:					Dict          =       original_model.trainable_variables
      SCREAMING_SNAKE_CASE_			:					int          =       original_model.non_trainable_variables
      SCREAMING_SNAKE_CASE_			:					str          =       {param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       param.numpy()
      SCREAMING_SNAKE_CASE_			:					Any          =       list(tf_params.keys()					)

      # Load HuggingFace model
      SCREAMING_SNAKE_CASE_			:					Any          =       get_align_config()
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       AlignModel(a					).eval()
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'					)
      SCREAMING_SNAKE_CASE_			:					str          =       rename_keys(a					)
      replace_params(a   ,				a   ,				a					)

      # Initialize processor
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       get_processor()
      SCREAMING_SNAKE_CASE_			:					List[str]          =       processor(
          images=prepare_img()   ,				text='A picture of a cat'   ,				padding='max_length'   ,				max_length=6_4   ,				return_tensors='pt'					)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       hf_model(**a					)

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       outputs.image_embeds.detach().numpy()
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       outputs.text_embeds.detach().numpy()

      # Original model inference
      SCREAMING_SNAKE_CASE_			:					Dict          =       False
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       EfficientNetImageProcessor(
          do_center_crop=a   ,				do_rescale=a   ,				do_normalize=a   ,				include_top=a   ,				resample=Image.BILINEAR   ,				)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       tf_image_processor(images=prepare_img()   ,				return_tensors='tf'   ,				data_format='channels_last'					)['pixel_values']
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       tok(tf.constant(['A picture of a cat']					)					)

      SCREAMING_SNAKE_CASE_			:					List[str]          =       original_model.image_encoder(a   ,				training=a					)
      SCREAMING_SNAKE_CASE_			:					Any          =       original_model.text_encoder(a   ,				training=a					)

      SCREAMING_SNAKE_CASE_			:					str          =       tf.nn.la_normalize(a   ,				axis=-1					)
      SCREAMING_SNAKE_CASE_			:					int          =       tf.nn.la_normalize(a   ,				axis=-1					)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(a   ,				a   ,				atol=1e-3					):
            raise ValueError('The predicted image features are not the same.'					)
      if not np.allclose(a   ,				a   ,				atol=1e-3					):
            raise ValueError('The predicted text features are not the same.'					)
      print('Model outputs match!'					)

      if save_model:
            # Create folder to save model
            if not os.path.isdir(a					):
                  os.mkdir(a					)
            # Save converted model and image processor
            hf_model.save_pretrained(a					)
            processor.save_pretrained(a					)

      if push_to_hub:
            # Push model and image processor to hub
            print('Pushing converted ALIGN to the hub...'					)
            processor.push_to_hub('align-base'					)
            hf_model.push_to_hub('align-base'					)


if __name__ == "__main__":
    lowerCAmelCase       :				str         =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint_path',
        default='./weights/model-weights',
        type=str,
        help='Path to the pretrained TF ALIGN checkpoint.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='hf_model',
        type=str,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--save_model', action='store_true', help='Save model to local')
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

    lowerCAmelCase       :				str         =					parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






