

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
				from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import GLPNImageProcessor
class 	_A						(					_lowerCamelCase):

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					List[Any]          =       self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(__UpperCamelCase	,   'hidden_sizes'   )   )
								self.parent.assertTrue(hasattr(__UpperCamelCase	,   'num_attention_heads'   )   )
								self.parent.assertTrue(hasattr(__UpperCamelCase	,   'num_encoder_blocks'   )   )

class 	_A						:

		def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=13	,   _SCREAMING_SNAKE_CASE=64	,   _SCREAMING_SNAKE_CASE=3	,   _SCREAMING_SNAKE_CASE=4	,   _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]	,   _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]	,   _SCREAMING_SNAKE_CASE=[16, 32, 64, 128]	,   _SCREAMING_SNAKE_CASE=[1, 4, 8, 16]	,   _SCREAMING_SNAKE_CASE=[1, 2, 4, 8]	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE="gelu"	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=0.02	,   _SCREAMING_SNAKE_CASE=16	,   _SCREAMING_SNAKE_CASE=3	,   _SCREAMING_SNAKE_CASE=None	,   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       parent
								SCREAMING_SNAKE_CASE_			:					Dict          =       batch_size
								SCREAMING_SNAKE_CASE_			:					int          =       image_size
								SCREAMING_SNAKE_CASE_			:					str          =       num_channels
								SCREAMING_SNAKE_CASE_			:					Dict          =       num_encoder_blocks
								SCREAMING_SNAKE_CASE_			:					List[str]          =       sr_ratios
								SCREAMING_SNAKE_CASE_			:					str          =       depths
								SCREAMING_SNAKE_CASE_			:					str          =       hidden_sizes
								SCREAMING_SNAKE_CASE_			:					Dict          =       downsampling_rates
								SCREAMING_SNAKE_CASE_			:					str          =       num_attention_heads
								SCREAMING_SNAKE_CASE_			:					List[str]          =       is_training
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       use_labels
								SCREAMING_SNAKE_CASE_			:					Any          =       hidden_act
								SCREAMING_SNAKE_CASE_			:					int          =       hidden_dropout_prob
								SCREAMING_SNAKE_CASE_			:					Tuple          =       attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_			:					Dict          =       initializer_range
								SCREAMING_SNAKE_CASE_			:					Tuple          =       decoder_hidden_size
								SCREAMING_SNAKE_CASE_			:					Any          =       num_labels
								SCREAMING_SNAKE_CASE_			:					Any          =       scope

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Tuple          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       None
								if self.use_labels:
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]	,   self.num_labels   )

								SCREAMING_SNAKE_CASE_			:					List[str]          =       self.get_config()
								return config, pixel_values, labels

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								return GLPNConfig(
								    image_size=self.image_size	,   num_channels=self.num_channels	,   num_encoder_blocks=self.num_encoder_blocks	,   depths=self.depths	,   hidden_sizes=self.hidden_sizes	,   num_attention_heads=self.num_attention_heads	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   initializer_range=self.initializer_range	,   decoder_hidden_size=self.decoder_hidden_size	,   )

		def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       GLPNModel(config=__UpperCamelCase   )
								model.to(__UpperCamelCase   )
								model.eval()
								SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model(__UpperCamelCase   )
								SCREAMING_SNAKE_CASE_			:					Any          =       self.image_size // (self.downsampling_rates[-1] * 2)
								self.parent.assertEqual(
								    result.last_hidden_state.shape	,   (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )

		def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Dict          =       self.num_labels
								SCREAMING_SNAKE_CASE_			:					Any          =       GLPNForDepthEstimation(__UpperCamelCase   )
								model.to(__UpperCamelCase   )
								model.eval()
								SCREAMING_SNAKE_CASE_			:					int          =       model(__UpperCamelCase   )
								self.parent.assertEqual(result.predicted_depth.shape	,   (self.batch_size, self.image_size, self.image_size)   )
								SCREAMING_SNAKE_CASE_			:					Dict          =       model(__UpperCamelCase	,   labels=__UpperCamelCase   )
								self.parent.assertEqual(result.predicted_depth.shape	,   (self.batch_size, self.image_size, self.image_size)   )



		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE_			:					Dict          =       config_and_inputs
								SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       {'pixel_values': pixel_values}
								return config, inputs_dict

@require_torch
class 	_A						(					_lowerCamelCase				,       _lowerCamelCase				,       unittest.TestCase):
		SCREAMING_SNAKE_CASE      : Union[str, Any]    =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE      : Optional[Any]    =					(
		    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
		)

		SCREAMING_SNAKE_CASE      : Any    =					False
		SCREAMING_SNAKE_CASE      : Union[str, Any]    =					False
		SCREAMING_SNAKE_CASE      : Optional[int]    =					False

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					List[Any]          =       GLPNModelTester(self   )
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       GLPNConfigTester(self	,   config_class=__UpperCamelCase   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								self.config_tester.run_common_tests()

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__UpperCamelCase   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					str          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase   )

		@unittest.skip('GLPN does not use inputs_embeds'   )
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								pass

		@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'   )
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								pass

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					int          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE_			:					Dict          =       model_class(__UpperCamelCase   )
														SCREAMING_SNAKE_CASE_			:					str          =       inspect.signature(model.forward   )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       [*signature.parameters.keys()]

														SCREAMING_SNAKE_CASE_			:					Dict          =       ['pixel_values']
														self.assertListEqual(arg_names[:1]	,   __UpperCamelCase   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					Any          =       self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_			:					List[Any]          =       True

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE_			:					int          =       True
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False
														SCREAMING_SNAKE_CASE_			:					int          =       True
														SCREAMING_SNAKE_CASE_			:					Tuple          =       model_class(__UpperCamelCase   )
														model.to(__UpperCamelCase   )
														model.eval()
														with torch.no_grad():
																				SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       model(**self._prepare_for_class(__UpperCamelCase	,   __UpperCamelCase   )   )
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       outputs.attentions

														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       sum(self.model_tester.depths   )
														self.assertEqual(len(__UpperCamelCase   )	,   __UpperCamelCase   )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       True
														SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model_class(__UpperCamelCase   )
														model.to(__UpperCamelCase   )
														model.eval()
														with torch.no_grad():
																				SCREAMING_SNAKE_CASE_			:					List[Any]          =       model(**self._prepare_for_class(__UpperCamelCase	,   __UpperCamelCase   )   )
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       outputs.attentions

														self.assertEqual(len(__UpperCamelCase   )	,   __UpperCamelCase   )

														# verify the first attentions (first block, first layer)
														SCREAMING_SNAKE_CASE_			:					Any          =       (self.model_tester.image_size // 4) ** 2
														SCREAMING_SNAKE_CASE_			:					Any          =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
														self.assertListEqual(
														    list(attentions[0].shape[-3:]   )	,   [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,   )

														# verify the last attentions (last block, last layer)
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       (self.model_tester.image_size // 32) ** 2
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
														self.assertListEqual(
														    list(attentions[-1].shape[-3:]   )	,   [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,   )
														SCREAMING_SNAKE_CASE_			:					List[str]          =       len(__UpperCamelCase   )

														# Check attention is always last and order is fine
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       True
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True
														SCREAMING_SNAKE_CASE_			:					List[str]          =       model_class(__UpperCamelCase   )
														model.to(__UpperCamelCase   )
														model.eval()
														with torch.no_grad():
																				SCREAMING_SNAKE_CASE_			:					Tuple          =       model(**self._prepare_for_class(__UpperCamelCase	,   __UpperCamelCase   )   )

														self.assertEqual(out_len + 1	,   len(__UpperCamelCase   )   )

														SCREAMING_SNAKE_CASE_			:					Tuple          =       outputs.attentions

														self.assertEqual(len(__UpperCamelCase   )	,   __UpperCamelCase   )
														# verify the first attentions (first block, first layer)
														SCREAMING_SNAKE_CASE_			:					List[str]          =       (self.model_tester.image_size // 4) ** 2
														SCREAMING_SNAKE_CASE_			:					str          =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]   )	,   [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								def check_hidden_states_output(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       model_class(__UpperCamelCase   )
														model.to(__UpperCamelCase   )
														model.eval()

														with torch.no_grad():
																				SCREAMING_SNAKE_CASE_			:					List[str]          =       model(**self._prepare_for_class(__UpperCamelCase	,   __UpperCamelCase   )   )

														SCREAMING_SNAKE_CASE_			:					List[Any]          =       outputs.hidden_states

														SCREAMING_SNAKE_CASE_			:					Any          =       self.model_tester.num_encoder_blocks
														self.assertEqual(len(__UpperCamelCase   )	,   __UpperCamelCase   )

														# verify the first hidden states (first block)
														self.assertListEqual(
														    list(hidden_states[0].shape[-3:]   )	,   [
														        self.model_tester.hidden_sizes[0],
														        self.model_tester.image_size // 4,
														        self.model_tester.image_size // 4,
														    ]	,   )

								SCREAMING_SNAKE_CASE_			:					Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True
														check_hidden_states_output(__UpperCamelCase	,   __UpperCamelCase	,   __UpperCamelCase   )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       True

														check_hidden_states_output(__UpperCamelCase	,   __UpperCamelCase	,   __UpperCamelCase   )

		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								if not self.model_tester.is_training:
														return

								SCREAMING_SNAKE_CASE_			:					int          =       self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       True

								for model_class in self.all_model_classes:
														if model_class in get_values(__UpperCamelCase   ):
																				continue
														# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
														# this can then be incorporated into _prepare_for_class in test_modeling_common.py
														if model_class.__name__ == "GLPNForDepthEstimation":
																				SCREAMING_SNAKE_CASE_			:					Optional[int]          =       inputs_dict['pixel_values'].shape
																				SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.zeros(
																				    [self.model_tester.batch_size, height, width]	,   device=__UpperCamelCase   ).long()
														SCREAMING_SNAKE_CASE_			:					Dict          =       model_class(__UpperCamelCase   )
														model.to(__UpperCamelCase   )
														model.train()
														SCREAMING_SNAKE_CASE_			:					List[Any]          =       self._prepare_for_class(__UpperCamelCase	,   __UpperCamelCase	,   return_labels=__UpperCamelCase   )
														SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       model(**__UpperCamelCase   ).loss
														loss.backward()



		@slow
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE_			:					List[str]          =       GLPNModel.from_pretrained(__UpperCamelCase   )
														self.assertIsNotNone(__UpperCamelCase   )

def 				A_					(							):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_			:					Any          =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
						return image



@require_torch
@require_vision
@slow
class 	_A						(					unittest.TestCase):

		@slow
		def 	UpperCAmelCase      (			self   ):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_			:					List[Any]          =       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
								SCREAMING_SNAKE_CASE_			:					int          =       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__UpperCamelCase   )

								SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       prepare_img()
								SCREAMING_SNAKE_CASE_			:					Any          =       image_processor(images=__UpperCamelCase	,   return_tensors='pt'   ).to(__UpperCamelCase   )

								# forward pass
								with torch.no_grad():
														SCREAMING_SNAKE_CASE_			:					Any          =       model(**__UpperCamelCase   )

								# verify the predicted depth
								SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.Size([1, 480, 640]   )
								self.assertEqual(outputs.predicted_depth.shape	,   __UpperCamelCase   )

								SCREAMING_SNAKE_CASE_			:					List[str]          =       torch.tensor(
								    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]   ).to(__UpperCamelCase   )

								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,   __UpperCamelCase	,   atol=1e-4   )   )









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)






