
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       A__  :





							def __init__(					self    :  List[Any]	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  Optional[int]=False	,					_UpperCAmelCase    :  int=13	,					_UpperCAmelCase    :  Any=(32, 1_28)	,					_UpperCAmelCase    :  Dict=4	,					_UpperCAmelCase    :  int=3	,					_UpperCAmelCase    :  List[str]=27	,					_UpperCAmelCase    :  List[Any]=38	,					_UpperCAmelCase    :  List[Any]=99	,					_UpperCAmelCase    :  Tuple=99	,					_UpperCAmelCase    :  Optional[Any]=32	,					_UpperCAmelCase    :  str=5	,					_UpperCAmelCase    :  List[str]=4	,					_UpperCAmelCase    :  List[str]=4.0	,					_UpperCAmelCase    :  int=2_57	,					_UpperCAmelCase    :  int=None	,					)		->						Dict:



											"""simple docstring"""


											__lowercase								=   parent
											__lowercase								=   is_training
											__lowercase								=   batch_size
											__lowercase								=   image_size
											__lowercase								=   patch_size
											__lowercase								=   num_channels
											__lowercase								=   max_token_length
											__lowercase								=   num_character_labels
											__lowercase								=   num_bpe_labels
											__lowercase								=   num_wordpiece_labels
											__lowercase								=   hidden_size
											__lowercase								=   num_hidden_layers
											__lowercase								=   num_attention_heads
											__lowercase								=   mlp_ratio
											__lowercase								=   patch_embeds_hidden_size
											__lowercase								=   output_hidden_states





							def      a__					(					self    :  Union[str, Any]							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)
											__lowercase								=   self.get_config()
											return config, pixel_values





							def      a__					(					self    :  Optional[int]							)		->						List[Any]:



											"""simple docstring"""


											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    :  Union[str, Any]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  Dict							)		->						Optional[Any]:



											"""simple docstring"""


											__lowercase								=   MgpstrForSceneTextRecognition(lowerCAmelCase__							)
											model.to(lowerCAmelCase__							)
											model.eval()
											with torch.no_grad():
															__lowercase								=   model(lowerCAmelCase__							)
											self.parent.assertEqual(
											    generated_ids[0][0].shape	,					(self.batch_size, self.max_token_length, self.num_character_labels)							)





							def      a__					(					self    :  Dict							)		->						List[str]:



											"""simple docstring"""


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






@require_torch
class       A__  (		lowerCAmelCase__       ,		lowerCAmelCase__       ,		unittest.TestCase							):
							lowerCAmelCase__						:		Dict            =				(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
							lowerCAmelCase__						:		Optional[int]            =				{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
							lowerCAmelCase__						:		Union[str, Any]            =				False

							lowerCAmelCase__						:		Optional[int]            =				False
							lowerCAmelCase__						:		int            =				False
							lowerCAmelCase__						:		int            =				False
							lowerCAmelCase__						:		Dict            =				False





							def      a__					(					self    :  Dict							)		->						str:



											"""simple docstring"""


											__lowercase								=   MgpstrModelTester(self							)
											__lowercase								=   ConfigTester(self	,					config_class=lowerCAmelCase__	,					has_text_modality=lowerCAmelCase__							)





							def      a__					(					self    :  Union[str, Any]							)		->						Dict:



											"""simple docstring"""


											self.config_tester.run_common_tests()





							def      a__					(					self    :  List[str]							)		->						Any:



											"""simple docstring"""


											__lowercase								=   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[Any]							)		->						List[str]:



											"""simple docstring"""


											pass





							def      a__					(					self    :  Union[str, Any]							)		->						List[str]:



											"""simple docstring"""


											__lowercase						,   __lowercase								=   self.model_tester.prepare_config_and_inputs_for_common()

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





							def      a__					(					self    :  Union[str, Any]							)		->						List[Any]:



											"""simple docstring"""


											__lowercase						,   __lowercase								=   self.model_tester.prepare_config_and_inputs_for_common()

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

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





							@unittest.skip(reason='MgpstrModel does not support feedforward chunking'							)
							def      a__					(					self    :  List[Any]							)		->						Any:



											"""simple docstring"""


											pass





							def      a__					(					self    :  int							)		->						int:



											"""simple docstring"""


											__lowercase						,   __lowercase								=   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

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





							def      a__					(					self    :  List[str]							)		->						List[str]:



											"""simple docstring"""


											def check_hidden_states_output(_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  Union[str, Any]							):
															__lowercase								=   model_class(lowerCAmelCase__							)
															model.to(lowerCAmelCase__							)
															model.eval()

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

															__lowercase								=   outputs.hidden_states

															__lowercase								=   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]	,					)

											__lowercase						,   __lowercase								=   self.model_tester.prepare_config_and_inputs_for_common()

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

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

															check_hidden_states_output(lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__							)





							def      a__					(					self    :  Tuple							)		->						Optional[int]:



											"""simple docstring"""


											__lowercase						,   __lowercase								=   self.model_tester.prepare_config_and_inputs_for_common()

											__lowercase								=   _config_zero_init(lowerCAmelCase__							)
											for model_class in self.all_model_classes:
															__lowercase								=   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    :  Optional[Any]							)		->						int:



											"""simple docstring"""


											pass






def 				__SCREAMING_SNAKE_CASE  (						)	->		Dict:
				__lowercase								=   'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
				__lowercase								=   Image.open(requests.get(SCREAMING_SNAKE_CASE       ,				stream=SCREAMING_SNAKE_CASE     ).raw     ).convert('RGB'     )
				return im
@require_vision
@require_torch
class       A__  (		unittest.TestCase							):





							@slow
							def      a__					(					self    :  Union[str, Any]							)		->						Any:



											"""simple docstring"""


											__lowercase								=   'alibaba-damo/mgp-str-base'
											__lowercase								=   MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase__							).to(lowerCAmelCase__							)
											__lowercase								=   MgpstrProcessor.from_pretrained(lowerCAmelCase__							)

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

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

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

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

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

											__lowercase								=   torch.tensor(
											    [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]]	,					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)

