




'''simple docstring'''







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()
A					       =logging.get_logger(__name__)





def 		snake_case_				(_a      :				int	):
    UpperCAmelCase     			=						tf.image.resize(lowercase_       ,  (3_4_6, 3_4_6)	)
    UpperCAmelCase     			=						tf.image.crop_to_bounding_box(lowercase_       ,  (3_4_6 - 2_8_9) // 2       ,  (3_4_6 - 2_8_9) // 2       ,  2_8_9       ,  2_8_9	)
    return image





def 		snake_case_				():
    UpperCAmelCase     			=						EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''	)
    UpperCAmelCase     			=						2_8_9
    UpperCAmelCase     			=						6_4_0
    UpperCAmelCase     			=						{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
    UpperCAmelCase     			=						{'''LABEL_0''': 0, '''LABEL_1''': 1}
    UpperCAmelCase     			=						[]

    UpperCAmelCase     			=						BertConfig()
    UpperCAmelCase     			=						AlignConfig.from_text_vision_configs(
        text_config=lowercase_       ,  vision_config=lowercase_       ,  projection_dim=6_4_0	)
    return config





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





def 		snake_case_				():
    UpperCAmelCase     			=						EfficientNetImageProcessor(
        do_center_crop=lowercase_       ,  rescale_factor=1 / 1_2_7.5       ,  rescale_offset=lowercase_       ,  do_normalize=lowercase_       ,  include_top=lowercase_       ,  resample=Image.BILINEAR       ,  )
    UpperCAmelCase     			=						BertTokenizer.from_pretrained('''bert-base-uncased'''	)
    UpperCAmelCase     			=						6_4
    UpperCAmelCase     			=						AlignProcessor(image_processor=lowercase_       ,  tokenizer=lowercase_	)
    return processor





def 		snake_case_				(_a      :				Union[str, Any]	):
    # EfficientNet image encoder
    UpperCAmelCase     			=						[v.split('''_'''	)[0].split('''block'''	)[1] for v in original_param_names if v.startswith('''block'''	)]
    UpperCAmelCase     			=						list(set(lowercase_	)	)
    UpperCAmelCase     			=						sorted(lowercase_	)
    UpperCAmelCase     			=						len(lowercase_	)
    UpperCAmelCase     			=						{b: str(lowercase_	) for b, i in zip(lowercase_       ,  range(lowercase_	)	)}

    UpperCAmelCase     			=						[]
    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:
        UpperCAmelCase     			=						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")	)

    UpperCAmelCase     			=						{}
    for item in rename_keys:
        if item[0] in original_param_names:
            UpperCAmelCase     			=						'''vision_model.''' + item[1]

    # BERT text encoder
    UpperCAmelCase     			=						[]
    UpperCAmelCase     			=						'''tf_bert_model/bert'''
    UpperCAmelCase     			=						'''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:
            UpperCAmelCase     			=						item[1]
    return key_mapping





def 		snake_case_				(_a      :				Union[str, Any]       ,  _a      :				Optional[int]       ,  _a      :				Union[str, Any]	):
    list(hf_params.keys()	)

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

        UpperCAmelCase     			=						key_mapping[key]
        if "_conv" in key and "kernel" in key:
            UpperCAmelCase     			=						torch.from_numpy(lowercase_	).permute(3       ,  2       ,  0       ,  1	)
        elif "embeddings" in key:
            UpperCAmelCase     			=						torch.from_numpy(lowercase_	)
        elif "depthwise_kernel" in key:
            UpperCAmelCase     			=						torch.from_numpy(lowercase_	).permute(2       ,  3       ,  0       ,  1	)
        elif "kernel" in key:
            UpperCAmelCase     			=						torch.from_numpy(np.transpose(lowercase_	)	)
        elif "temperature" in key:
            UpperCAmelCase     			=						value
        elif "bn/gamma" or "bn/beta" in key:
            UpperCAmelCase     			=						torch.from_numpy(np.transpose(lowercase_	)	).squeeze()
        else:
            UpperCAmelCase     			=						torch.from_numpy(lowercase_	)

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





@torch.no_grad()
def 		snake_case_				(_a      :				Any       ,  _a      :				List[Any]       ,  _a      :				List[str]       ,  _a      :				Dict	):
    UpperCAmelCase     			=						6_4
    UpperCAmelCase     			=						Tokenizer(lowercase_	)
    UpperCAmelCase     			=						align.Align('''efficientnet-b7'''       ,  '''bert-base'''       ,  6_4_0       ,  lowercase_       ,  tok.get_vocab_size()	)
    original_model.compile()
    original_model.load_weights(lowercase_	)

    UpperCAmelCase     			=						original_model.trainable_variables
    UpperCAmelCase     			=						original_model.non_trainable_variables
    UpperCAmelCase     			=						{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        UpperCAmelCase     			=						param.numpy()
    UpperCAmelCase     			=						list(tf_params.keys()	)

    # Load HuggingFace model
    UpperCAmelCase     			=						get_align_config()
    UpperCAmelCase     			=						AlignModel(lowercase_	).eval()
    UpperCAmelCase     			=						hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''	)
    UpperCAmelCase     			=						rename_keys(lowercase_	)
    replace_params(lowercase_       ,  lowercase_       ,  lowercase_	)

    # Initialize processor
    UpperCAmelCase     			=						get_processor()
    UpperCAmelCase     			=						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():
        UpperCAmelCase     			=						hf_model(**lowercase_	)

    UpperCAmelCase     			=						outputs.image_embeds.detach().numpy()
    UpperCAmelCase     			=						outputs.text_embeds.detach().numpy()

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

    UpperCAmelCase     			=						original_model.image_encoder(lowercase_       ,  training=lowercase_	)
    UpperCAmelCase     			=						original_model.text_encoder(lowercase_       ,  training=lowercase_	)

    UpperCAmelCase     			=						tf.nn.la_normalize(lowercase_       ,  axis=-1	)
    UpperCAmelCase     			=						tf.nn.la_normalize(lowercase_       ,  axis=-1	)

    # Check whether original and HF model outputs match  -> np.allclose
    if not np.allclose(lowercase_       ,  lowercase_       ,  atol=1E-3	):
        raise ValueError('''The predicted image features are not the same.'''	)
    if not np.allclose(lowercase_       ,  lowercase_       ,  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(lowercase_	):
            os.mkdir(lowercase_	)
        # Save converted model and image processor
        hf_model.save_pretrained(lowercase_	)
        processor.save_pretrained(lowercase_	)

    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__":
   A					       =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')

   A					       =parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





"""simple docstring"""







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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_attention_heads'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_encoder_blocks'	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   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'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					num_encoder_blocks
      __lowerCamelCase    		=					sr_ratios
      __lowerCamelCase    		=					depths
      __lowerCamelCase    		=					hidden_sizes
      __lowerCamelCase    		=					downsampling_rates
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					decoder_hidden_size
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					scope







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels	)

      __lowerCamelCase    		=					self.get_config()
      return config, pixel_values, labels







   def    _A				(	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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					__lowerCamelCase    		=					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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					config_and_inputs
      __lowerCamelCase    		=					{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   __UpperCAmelCase	:							Any    =    (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   __UpperCAmelCase	:							Optional[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False
   __UpperCAmelCase	:							int    =    False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModelTester(self	)
      __lowerCamelCase    		=					GLPNConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE	)







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


      '''simple docstring'''

      pass







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


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __lowerCamelCase    		=					[*signature.parameters.keys()]

         __lowerCamelCase    		=					['pixel_values']
         self.assertListEqual(arg_names[:1] ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					False
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         __lowerCamelCase    		=					sum(self.model_tester.depths	)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

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

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(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)
         __lowerCamelCase    		=					(self.model_tester.image_size // 32) ** 2
         __lowerCamelCase    		=					(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] ,				)
         __lowerCamelCase    		=					len(_SCREAMING_SNAKE_CASE	)

         # Check attention is always last and order is fine
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

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

         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)
         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(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    _A				(	self	):


      '''simple docstring'''

      def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()

         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.hidden_states

         __lowerCamelCase    		=					self.model_tester.num_encoder_blocks
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # 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,
             ] ,				)

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __lowerCamelCase    		=					True

         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         if model_class in get_values(_SCREAMING_SNAKE_CASE	):
            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":
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					inputs_dict['pixel_values'].shape
            __lowerCamelCase    		=					torch.zeros(
                [self.model_tester.batch_size, height, width] ,				device=_SCREAMING_SNAKE_CASE	).long()
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.train()
         __lowerCamelCase    		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				return_labels=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	).loss
         loss.backward()







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image






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



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      __lowerCamelCase    		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					prepare_img()
      __lowerCamelCase    		=					image_processor(images=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

      # forward pass
      with torch.no_grad():
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	)

      # verify the predicted depth
      __lowerCamelCase    		=					torch.Size([1, 480, 640]	)
      self.assertEqual(outputs.predicted_depth.shape ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	).to(_SCREAMING_SNAKE_CASE	)

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






