"""simple docstring"""


import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor




class 					UpperCamelCase   :







       def __init__(					self,				lowerCAmelCase__,				lowerCAmelCase__=False,				lowerCAmelCase__=13,				lowerCAmelCase__=(32, 128),				lowerCAmelCase__=4,				lowerCAmelCase__=3,				lowerCAmelCase__=27,				lowerCAmelCase__=38,				lowerCAmelCase__=99,				lowerCAmelCase__=99,				lowerCAmelCase__=32,				lowerCAmelCase__=5,				lowerCAmelCase__=4,				lowerCAmelCase__=4.0,				lowerCAmelCase__=257,				lowerCAmelCase__=None,				) -> Dict:
            snake_case_   			=   parent
            snake_case_   			=   is_training
            snake_case_   			=   batch_size
            snake_case_   			=   image_size
            snake_case_   			=   patch_size
            snake_case_   			=   num_channels
            snake_case_   			=   max_token_length
            snake_case_   			=   num_character_labels
            snake_case_   			=   num_bpe_labels
            snake_case_   			=   num_wordpiece_labels
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   mlp_ratio
            snake_case_   			=   patch_embeds_hidden_size
            snake_case_   			=   output_hidden_states







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
            snake_case_   			=   self.get_config()
            return config, pixel_values







       def 						a_		(					self) -> List[Any]:
            return MgpstrConfig(
                image_size=self.image_size,				patch_size=self.patch_size,				num_channels=self.num_channels,				max_token_length=self.max_token_length,				num_character_labels=self.num_character_labels,				num_bpe_labels=self.num_bpe_labels,				num_wordpiece_labels=self.num_wordpiece_labels,				hidden_size=self.hidden_size,				num_hidden_layers=self.num_hidden_layers,				num_attention_heads=self.num_attention_heads,				mlp_ratio=self.mlp_ratio,				output_hidden_states=self.output_hidden_states,				)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Optional[Any]:
            snake_case_   			=   MgpstrForSceneTextRecognition(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            with torch.no_grad():
                 snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(
                generated_ids[0][0].shape,				(self.batch_size, self.max_token_length, self.num_character_labels))







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   self.prepare_config_and_inputs()
            snake_case_			,      snake_case_   			=   config_and_inputs
            snake_case_   			=   {'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class 					UpperCamelCase   (							lowerCAmelCase__       ,						lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_						      = {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
       SCREAMING_SNAKE_CASE_						      = False

       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False







       def 						a_		(					self) -> str:
            snake_case_   			=   MgpstrModelTester(self)
            snake_case_   			=   ConfigTester(self,				config_class=lowerCAmelCase__,				has_text_modality=lowerCAmelCase__)







       def 						a_		(					self) -> Dict:
            self.config_tester.run_common_tests()







       def 						a_		(					self) -> Any:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__)







       @unittest.skip(reason='MgpstrModel does not use inputs_embeds')
       def 						a_		(					self) -> List[str]:
            pass







       def 						a_		(					self) -> List[str]:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 snake_case_   			=   model_class(lowerCAmelCase__)
                 self.assertIsInstance(model.get_input_embeddings(),				(nn.Module))
                 snake_case_   			=   model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCAmelCase__,				nn.Linear))







       def 						a_		(					self) -> List[Any]:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case_   			=   ['pixel_values']
                 self.assertListEqual(arg_names[:1],				lowerCAmelCase__)







       @unittest.skip(reason='MgpstrModel does not support feedforward chunking')
       def 						a_		(					self) -> Any:
            pass







       def 						a_		(					self) -> int:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 if not model_class.supports_gradient_checkpointing:
                      continue

                 snake_case_   			=   True
                 snake_case_   			=   model_class(lowerCAmelCase__)
                 self.assertTrue(model.is_gradient_checkpointing)







       def 						a_		(					self) -> List[str]:
            def check_hidden_states_output(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__):
                 snake_case_   			=   model_class(lowerCAmelCase__)
                 model.to(lowerCAmelCase__)
                 model.eval()

                 with torch.no_grad():
                      snake_case_   			=   model(**self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__))

                 snake_case_   			=   outputs.hidden_states

                 snake_case_   			=   getattr(
                     self.model_tester,				'expected_num_hidden_layers',				self.model_tester.num_hidden_layers + 1)
                 self.assertEqual(len(lowerCAmelCase__),				lowerCAmelCase__)

                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]),				[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size],				)

            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()

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

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

                 check_hidden_states_output(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)







       def 						a_		(					self) -> Optional[int]:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()

            snake_case_   			=   _config_zero_init(lowerCAmelCase__)
            for model_class in self.all_model_classes:
                 snake_case_   			=   model_class(config=lowerCAmelCase__)
                 for name, param in model.named_parameters():
                      if isinstance(lowerCAmelCase__,				(nn.Linear, nn.Convad, nn.LayerNorm)):
                           if param.requires_grad:
                                self.assertIn(
                                    ((param.data.mean() * 1e9).round() / 1e9).item(),				[0.0, 1.0],				msg=f'Parameter {name} of model {model_class} seems not properly initialized',				)







       @unittest.skip(reason='Retain_grad is tested in individual model tests')
       def 						a_		(					self) -> int:
            pass







def    UpperCAmelCase       (						)							-> Dict:
     snake_case_   			=   'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
     snake_case_   			=   Image.open(requests.get(UpperCAmelCase		,   stream=UpperCAmelCase   ).raw   ).convert('RGB'   )
     return im




@require_vision
@require_torch
class 					UpperCamelCase   (							unittest.TestCase  ):







       @slow
       def 						a_		(					self) -> Any:
            snake_case_   			=   'alibaba-damo/mgp-str-base'
            snake_case_   			=   MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase__).to(lowerCAmelCase__)
            snake_case_   			=   MgpstrProcessor.from_pretrained(lowerCAmelCase__)

            snake_case_   			=   prepare_img()
            snake_case_   			=   processor(images=lowerCAmelCase__,				return_tensors='pt').pixel_values.to(lowerCAmelCase__)

            # forward pass
            with torch.no_grad():
                 snake_case_   			=   model(lowerCAmelCase__)

            # verify the logits
            self.assertEqual(outputs.logits[0].shape,				torch.Size((1, 27, 38)))

            snake_case_   			=   processor.batch_decode(outputs.logits)
            snake_case_   			=   'ticket'

            self.assertEqual(out_strs['generated_text'][0],				lowerCAmelCase__)

            snake_case_   			=   torch.tensor(
                [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]],				device=lowerCAmelCase__,				)

            self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4],				lowerCAmelCase__,				atol=1e-4))


"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # 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       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   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       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   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
     snake_case_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   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       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

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

     hf_model.save_pretrained(UpperCAmelCase   )


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)

