






"""simple docstring"""



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_weight.chunk(3						,   dim=0       )
  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_bias.chunk(3						,   dim=0       )

  lowercase				       =			pt_attn_layer.out_proj.weight
  lowercase				       =			pt_attn_layer.out_proj.bias

  lowercase				       =			q_proj
  lowercase				       =			q_proj_bias

  lowercase				       =			k_proj
  lowercase				       =			k_proj_bias

  lowercase				       =			v_proj
  lowercase				       =			v_proj_bias

  lowercase				       =			out_proj_weights
  lowercase				       =			out_proj_bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    str       ):





  '''simple docstring'''





  copy_linear(hf_mlp.fca						,   pt_mlp.c_fc       )
  copy_linear(hf_mlp.fca						,   pt_mlp.c_proj       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			pt_linear.weight
  lowercase				       =			pt_linear.bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  for hf_layer, pt_layer in zip(__snake_case						,   __snake_case       ):
    copy_layer(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			pt_model.token_embedding.weight
  lowercase				       =			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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[Any]						,   __snake_case     :    List[str]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			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
  lowercase				       =			pt_model.visual.conva.weight.data
  lowercase				       =			pt_model.visual.class_embedding
  lowercase				       =			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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    Any						,   __snake_case     :    int=None       ):





  '''simple docstring'''





  if config_path is not None:
    lowercase				       =			CLIPConfig.from_pretrained(__snake_case       )
  else:
    lowercase				       =			CLIPConfig(projection_dim=5_12						,   text_config={}						,   vision_config={}       )

  lowercase				       =			CLIPModel(__snake_case       ).eval()

  lowercase       ,	lowercase				       =			load(__snake_case						,   device='cpu'						,   jit=__snake_case       )
  lowercase				       =			pt_model.eval()

  copy_text_model_and_projection(__snake_case						,   __snake_case       )
  copy_vison_model_and_projection(__snake_case						,   __snake_case       )
  lowercase				       =			pt_model.logit_scale

  lowercase				       =			torch.arange(0						,   77       ).unsqueeze(0       )
  lowercase				       =			torch.randn(1						,   3						,   2_24						,   2_24       )

  lowercase				       =			hf_model(input_ids=__snake_case						,   pixel_values=__snake_case						,   return_dict=__snake_case       )
  lowercase				       =			hf_outputs.logits_per_image
  lowercase				       =			hf_outputs.logits_per_text
  lowercase       ,	lowercase				       =			pt_model(__snake_case						,   __snake_case       )

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

  hf_model.save_pretrained(__snake_case       )


if __name__ == "__main__":
    _UpperCamelCase						:    int		=						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						:    Dict		=						parser.parse_args()

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










"""simple docstring"""



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor_slow.tokenizer.get_vocab()				,						tokenizer_slow.get_vocab()			)
      self.assertEqual(processor_fast.tokenizer.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertEqual(tokenizer_slow.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertIsInstance(processor_slow.tokenizer				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor_slow.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertEqual(processor_fast.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertIsInstance(processor_slow.image_processor				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase			):
        processor()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						processor.model_input_names			)


