
'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
     from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor


class 					a_					(_a      ):




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.config_class(**self.inputs_dict					)
         self.parent.assertTrue(hasattr(snake_case_				,				"""hidden_sizes"""					)					)
         self.parent.assertTrue(hasattr(snake_case_				,				"""neck_hidden_sizes"""					)					)
         self.parent.assertTrue(hasattr(snake_case_				,				"""num_attention_heads"""					)					)



class 					a_					:




       def __init__(       self				,				snake_case_				,				snake_case_=1_3				,				snake_case_=3_2				,				snake_case_=2				,				snake_case_=3				,				snake_case_=6_4_0				,				snake_case_=4				,				snake_case_="silu"				,				snake_case_=3				,				snake_case_=3_2				,				snake_case_=0.1				,				snake_case_=0.1				,				snake_case_=0.1				,				snake_case_=0.02				,				snake_case_=True				,				snake_case_=True				,				snake_case_=1_0				,				snake_case_=None				,				):
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					str							=   batch_size
         _lowerCAmelCase    :					Optional[Any]							=   image_size
         _lowerCAmelCase    :					Optional[Any]							=   patch_size
         _lowerCAmelCase    :					Optional[Any]							=   num_channels
         _lowerCAmelCase    :					Optional[int]							=   last_hidden_size
         _lowerCAmelCase    :					int							=   num_attention_heads
         _lowerCAmelCase    :					Optional[int]							=   hidden_act
         _lowerCAmelCase    :					Dict							=   conv_kernel_size
         _lowerCAmelCase    :					Any							=   output_stride
         _lowerCAmelCase    :					str							=   hidden_dropout_prob
         _lowerCAmelCase    :					str							=   attention_probs_dropout_prob
         _lowerCAmelCase    :					int							=   classifier_dropout_prob
         _lowerCAmelCase    :					Tuple							=   use_labels
         _lowerCAmelCase    :					str							=   is_training
         _lowerCAmelCase    :					Dict							=   num_labels
         _lowerCAmelCase    :					Tuple							=   initializer_range
         _lowerCAmelCase    :					int							=   scope




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

         _lowerCAmelCase    :					List[str]							=   self.get_config()

         return config, pixel_values, labels, pixel_labels




       def 							__UpperCamelCase    (       self					):
         return MobileViTConfig(
             image_size=self.image_size				,				patch_size=self.patch_size				,				num_channels=self.num_channels				,				num_attention_heads=self.num_attention_heads				,				hidden_act=self.hidden_act				,				conv_kernel_size=self.conv_kernel_size				,				output_stride=self.output_stride				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				classifier_dropout_prob=self.classifier_dropout_prob				,				initializer_range=self.initializer_range				,				)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					List[str]							=   TFMobileViTModel(config=snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   model(snake_case_				,				training=snake_case_					)
         _lowerCAmelCase    :					Dict							=   self.image_size // self.output_stride
         self.parent.assertEqual(
             result.last_hidden_state.shape				,				(self.batch_size, self.last_hidden_size, expected_height, expected_width)					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					List[str]							=   self.num_labels
         _lowerCAmelCase    :					List[Any]							=   TFMobileViTForImageClassification(snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   model(snake_case_				,				labels=snake_case_				,				training=snake_case_					)
         self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					int							=   self.num_labels
         _lowerCAmelCase    :					Union[str, Any]							=   TFMobileViTForSemanticSegmentation(snake_case_					)
         _lowerCAmelCase    :					str							=   self.image_size // self.output_stride

         _lowerCAmelCase    :					Union[str, Any]							=   model(snake_case_				,				training=snake_case_					)
         self.parent.assertEqual(
             result.logits.shape				,				(self.batch_size, self.num_labels, expected_height, expected_width)					)

         _lowerCAmelCase    :					Optional[Any]							=   model(snake_case_				,				labels=snake_case_				,				training=snake_case_					)
         self.parent.assertEqual(
             result.logits.shape				,				(self.batch_size, self.num_labels, expected_height, expected_width)					)







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   self.prepare_config_and_inputs()
         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					str							=   config_and_inputs
         _lowerCAmelCase    :					Optional[int]							=   {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_tf
class 					a_					(_a							,     _a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Union[str, Any]      = (
           (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
           if is_tf_available()
           else ()
       )
       __lowerCAmelCase      :			Optional[Any]      = (
           {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
           if is_tf_available()
           else {}
       )

       __lowerCAmelCase      :			Tuple      = False
       __lowerCAmelCase      :			Tuple      = False
       __lowerCAmelCase      :			Any      = False
       __lowerCAmelCase      :			List[str]      = False
       __lowerCAmelCase      :			Dict      = False




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   TFMobileViTModelTester(self					)
         _lowerCAmelCase    :					Optional[Any]							=   TFMobileViTConfigTester(self				,				config_class=snake_case_				,				has_text_modality=snake_case_					)




       def 							__UpperCamelCase    (       self					):
         self.config_tester.run_common_tests()




       @unittest.skip(reason="""MobileViT does not use inputs_embeds"""					)
       def 							__UpperCamelCase    (       self					):
         pass




       @unittest.skip(reason="""MobileViT does not support input and output embeddings"""					)
       def 							__UpperCamelCase    (       self					):
         pass




       @unittest.skip(reason="""MobileViT does not output attentions"""					)
       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowerCAmelCase    :					Optional[Any]							=   model_class(snake_case_					)
           _lowerCAmelCase    :					Union[str, Any]							=   inspect.signature(model.call					)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowerCAmelCase    :					Any							=   [*signature.parameters.keys()]

           _lowerCAmelCase    :					Optional[Any]							=   ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[str]							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         def check_hidden_states_output(snake_case_				,				snake_case_				,				snake_case_					):
           _lowerCAmelCase    :					List[Any]							=   model_class(snake_case_					)

           _lowerCAmelCase    :					List[str]							=   model(**self._prepare_for_class(snake_case_				,				snake_case_					)					)

           _lowerCAmelCase    :					Union[str, Any]							=   outputs.hidden_states

           _lowerCAmelCase    :					Optional[Any]							=   5
           self.assertEqual(len(snake_case_					)				,				snake_case_					)

           # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           _lowerCAmelCase    :					Union[str, Any]							=   2
           for i in range(len(snake_case_					)					):
             self.assertListEqual(
                 list(hidden_states[i].shape[-2:]					)				,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]				,				)
             divisor *= 2

           self.assertEqual(self.model_tester.output_stride				,				divisor // 2					)

         _lowerCAmelCase			,   _lowerCAmelCase    :					Dict							=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowerCAmelCase    :					Tuple							=   True
           check_hidden_states_output(snake_case_				,				snake_case_				,				snake_case_					)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowerCAmelCase    :					int							=   True

           check_hidden_states_output(snake_case_				,				snake_case_				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_					)




       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0				,				reason="""TF does not support backprop for grouped convolutions on CPU."""				,				)
       def 							__UpperCamelCase    (       self					):
         super().test_dataset_conversion()




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_=2E-1				,				snake_case_=2E-1					):
         self.assertTrue(np.allclose(snake_case_				,				snake_case_				,				atol=snake_case_				,				rtol=snake_case_					)					)




       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0				,				reason="""TF does not support backprop for grouped convolutions on CPU."""				,				)
       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase			,   _lowerCAmelCase    :					Any							=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
           if model_class.__name__ != "TFMobileViTModel":
             _lowerCAmelCase    :					int							=   model_class(snake_case_					)
             if getattr(snake_case_				,				"""hf_compute_loss"""				,				snake_case_					):
               super().test_keras_fit()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           # set an ignore index to correctly test the masked loss used in
           # `TFMobileViTForSemanticSegmentation`.
           if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
             _lowerCAmelCase    :					Union[str, Any]							=   5

           _lowerCAmelCase    :					Union[str, Any]							=   model_class(snake_case_					)
           if getattr(snake_case_				,				"""hf_compute_loss"""				,				snake_case_					):
             # The number of elements in the loss should be the same as the number of elements in the label
             _lowerCAmelCase    :					List[str]							=   self._prepare_for_class(inputs_dict.copy()				,				snake_case_				,				return_labels=snake_case_					)
             _lowerCAmelCase    :					int							=   prepared_for_class[
                 sorted(prepared_for_class.keys() - inputs_dict.keys()				,				reverse=snake_case_					)[0]
             ]
             _lowerCAmelCase    :					List[str]							=   added_label.shape.as_list()[:1]

             # Test that model correctly compute the loss with kwargs
             _lowerCAmelCase    :					int							=   self._prepare_for_class(inputs_dict.copy()				,				snake_case_				,				return_labels=snake_case_					)
             _lowerCAmelCase    :					Optional[Any]							=   {"""input_ids""", """pixel_values""", """input_features"""}
             _lowerCAmelCase    :					Union[str, Any]							=   possible_input_names.intersection(set(snake_case_					)					).pop()
             _lowerCAmelCase    :					Optional[int]							=   prepared_for_class.pop(snake_case_					)

             _lowerCAmelCase    :					Union[str, Any]							=   model(snake_case_				,				**snake_case_					)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

             # Test that model correctly compute the loss when we mask some positions
             _lowerCAmelCase    :					List[str]							=   self._prepare_for_class(inputs_dict.copy()				,				snake_case_				,				return_labels=snake_case_					)
             _lowerCAmelCase    :					List[Any]							=   {"""input_ids""", """pixel_values""", """input_features"""}
             _lowerCAmelCase    :					List[str]							=   possible_input_names.intersection(set(snake_case_					)					).pop()
             _lowerCAmelCase    :					Union[str, Any]							=   prepared_for_class.pop(snake_case_					)
             if "labels" in prepared_for_class:
               _lowerCAmelCase    :					List[str]							=   prepared_for_class["""labels"""].numpy()
               if len(labels.shape					) > 1 and labels.shape[1] != 1:
                 # labels[0] = -100
                 _lowerCAmelCase    :					int							=   tf.convert_to_tensor(snake_case_					)
                 _lowerCAmelCase    :					Optional[Any]							=   model(snake_case_				,				**snake_case_					)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
                 self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
             _lowerCAmelCase    :					Any							=   self._prepare_for_class(inputs_dict.copy()				,				snake_case_				,				return_labels=snake_case_					)
             _lowerCAmelCase    :					Union[str, Any]							=   model(snake_case_					)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

             # Test that model correctly compute the loss with a tuple
             _lowerCAmelCase    :					Optional[Any]							=   self._prepare_for_class(inputs_dict.copy()				,				snake_case_				,				return_labels=snake_case_					)

             # Get keys that were added with the _prepare_for_class function
             _lowerCAmelCase    :					Optional[int]							=   prepared_for_class.keys() - inputs_dict.keys()
             _lowerCAmelCase    :					Any							=   inspect.signature(model.call					).parameters
             _lowerCAmelCase    :					str							=   list(signature.keys()					)

             # Create a dictionary holding the location of the tensors in the tuple
             _lowerCAmelCase    :					Optional[Any]							=   {0: input_name}
             for label_key in label_keys:
               _lowerCAmelCase    :					List[Any]							=   signature_names.index(snake_case_					)
               _lowerCAmelCase    :					Dict							=   label_key
             _lowerCAmelCase    :					Optional[Any]							=   sorted(tuple_index_mapping.items()					)
             # Initialize a list with their default values, update the values and convert to a tuple
             _lowerCAmelCase    :					Optional[int]							=   []

             for name in signature_names:
               if name != "kwargs":
                 list_input.append(signature[name].default					)

             for index, value in sorted_tuple_index_mapping:
               _lowerCAmelCase    :					Dict							=   prepared_for_class[value]

             _lowerCAmelCase    :					int							=   tuple(snake_case_					)

             # Send to model
             _lowerCAmelCase    :					Tuple							=   model(tuple_input[:-1]					)[0]

             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)







       @slow
       def 							__UpperCamelCase    (       self					):
         for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase    :					int							=   TFMobileViTModel.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)



def 		_UpperCAmelCase			(					)      ->  Tuple:
  _lowerCAmelCase    :					int							=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image



@require_tf
class 					a_					(unittest.TestCase      ):




       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[str]							=   TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""					)

         _lowerCAmelCase    :					Union[str, Any]							=   MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""					)
         _lowerCAmelCase    :					int							=   prepare_img()
         _lowerCAmelCase    :					Optional[int]							=   image_processor(images=snake_case_				,				return_tensors="""tf"""					)

         # forward pass
         _lowerCAmelCase    :					str							=   model(**snake_case_				,				training=snake_case_					)

         # verify the logits
         _lowerCAmelCase    :					List[Any]							=   tf.TensorShape((1, 1_0_0_0)					)
         self.assertEqual(outputs.logits.shape				,				snake_case_					)

         _lowerCAmelCase    :					Optional[int]							=   tf.constant([-1.9364, -1.2327, -0.4653]					)

         tf.debugging.assert_near(outputs.logits[0, :3]				,				snake_case_				,				atol=1E-4				,				rtol=1E-04					)







       @slow
       def 							__UpperCamelCase    (       self					):
         # `from_pt` will be removed
         _lowerCAmelCase    :					Union[str, Any]							=   TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

         _lowerCAmelCase    :					str							=   MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

         _lowerCAmelCase    :					List[str]							=   prepare_img()
         _lowerCAmelCase    :					Union[str, Any]							=   image_processor(images=snake_case_				,				return_tensors="""tf"""					)

         # forward pass
         _lowerCAmelCase    :					Union[str, Any]							=   model(inputs.pixel_values				,				training=snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   outputs.logits

         # verify the logits
         _lowerCAmelCase    :					List[Any]							=   tf.TensorShape((1, 2_1, 3_2, 3_2)					)
         self.assertEqual(logits.shape				,				snake_case_					)

         _lowerCAmelCase    :					List[str]							=   tf.constant(
             [
                 [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                 [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                 [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
             ]					)

         tf.debugging.assert_near(logits[0, :3, :3, :3]				,				snake_case_				,				rtol=1E-4				,				atol=1E-4					)




'''simple docstring'''

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Dict						)      ->  List[Any]:
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.in_proj_weight.chunk(3					, dim=0						)
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   pt_attn_layer.in_proj_bias.chunk(3					, dim=0						)

  _lowerCAmelCase    :					Optional[Any]							=   pt_attn_layer.out_proj.weight
  _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.out_proj.bias

  _lowerCAmelCase    :					Union[str, Any]							=   q_proj
  _lowerCAmelCase    :					Optional[Any]							=   q_proj_bias

  _lowerCAmelCase    :					List[Any]							=   k_proj
  _lowerCAmelCase    :					Optional[int]							=   k_proj_bias

  _lowerCAmelCase    :					int							=   v_proj
  _lowerCAmelCase    :					Dict							=   v_proj_bias

  _lowerCAmelCase    :					str							=   out_proj_weights
  _lowerCAmelCase    :					Union[str, Any]							=   out_proj_bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    List[Any]						)      ->  Dict:
  copy_linear(hf_mlp.fca					, pt_mlp.c_fc						)
  copy_linear(hf_mlp.fca					, pt_mlp.c_proj						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]						)      ->  Optional[Any]:
  _lowerCAmelCase    :					List[str]							=   pt_linear.weight
  _lowerCAmelCase    :					Union[str, Any]							=   pt_linear.bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    int						)      ->  Optional[Any]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)

  # copy MLP
  copy_mlp(hf_layer.mlp					, pt_layer.mlp						)

  # copy attn
  copy_attn_layer(hf_layer.self_attn					, pt_layer.attn						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Union[str, Any]						)      ->  Optional[Any]:
  for hf_layer, pt_layer in zip(_lowerCamelCase					, _lowerCamelCase						):
    copy_layer(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  # copy  embeds
  _lowerCAmelCase    :					Optional[int]							=   pt_model.token_embedding.weight
  _lowerCAmelCase    :					List[str]							=   pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm					, pt_model.ln_final						)

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers					, pt_model.transformer.resblocks						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Tuple						)      ->  Tuple:
  # copy projection
  _lowerCAmelCase    :					Any							=   pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  # copy projection
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm					, pt_model.visual.ln_pre						)
  copy_linear(hf_model.vision_model.post_layernorm					, pt_model.visual.ln_post						)

  # copy embeds
  _lowerCAmelCase    :					List[Any]							=   pt_model.visual.conva.weight.data
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.class_embedding
  _lowerCAmelCase    :					int							=   pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers					, pt_model.visual.transformer.resblocks						)


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Any=None						)      ->  int:
  if config_path is not None:
    _lowerCAmelCase    :					Optional[int]							=   CLIPConfig.from_pretrained(_lowerCamelCase						)
  else:
    _lowerCAmelCase    :					int							=   CLIPConfig(projection_dim=5_12					, text_config={}					, vision_config={}						)

  _lowerCAmelCase    :					Optional[int]							=   CLIPModel(_lowerCamelCase						).eval()

  _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   load(_lowerCamelCase					, device="""cpu"""					, jit=_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   pt_model.eval()

  copy_text_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  copy_vison_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  _lowerCAmelCase    :					Tuple							=   pt_model.logit_scale

  _lowerCAmelCase    :					List[str]							=   torch.arange(0					, 77						).unsqueeze(0						)
  _lowerCAmelCase    :					int							=   torch.randn(1					, 3					, 2_24					, 2_24						)

  _lowerCAmelCase    :					List[str]							=   hf_model(input_ids=_lowerCamelCase					, pixel_values=_lowerCamelCase					, return_dict=_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   hf_outputs.logits_per_image
  _lowerCAmelCase    :					List[str]							=   hf_outputs.logits_per_text
  _lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   pt_model(_lowerCamelCase					, _lowerCamelCase						)

  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)
  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)

  hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     UpperCamelCase_            =    parser.parse_args()

     convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


