



import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
						from transformers import (
						    FlaxBertModel,
						    FlaxCLIPVisionModel,
						    FlaxVisionTextDualEncoderModel,
						    FlaxViTModel,
						    VisionTextDualEncoderConfig,
						    VisionTextDualEncoderProcessor,
						)
						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)


if is_torch_available():
						import torch

						from transformers import VisionTextDualEncoderModel

if is_vision_available():
						from PIL import Image


def 			UpperCamelCase(  __UpperCamelCase							:		Dict ):
		if isinstance(__UpperCamelCase     ,collections.abc.Iterable ):
				return x
		return (x, x)



@require_flax
class 				__snake_case						:




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       str				,				A_     :       List[str]):
									pass




							def    UpperCAmelCase__			(       self     :       int):
									pass




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									pass




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       np.ndarray				,				A_     :       np.ndarray				,				A_     :       float):
									lowerCAmelCase_			:     Tuple									= np.abs((a - b)).max()
									self.assertLessEqual(A_				,				A_				,				F"""Difference between torch and flax is {diff} (>= {tol}).""")




							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       List[str]				,				A_     :       Tuple				,				A_     :       Optional[int]				,				A_     :       List[str]				,				A_     :       Optional[int]=None				,				**A_     :       Union[str, Any]):
									lowerCAmelCase_			:     Optional[Any]									= VisionTextDualEncoderConfig.from_vision_text_configs(A_				,				A_)

									lowerCAmelCase_			:     str									= FlaxVisionTextDualEncoderModel(A_)

									lowerCAmelCase_			:     Union[str, Any]									= model(input_ids=A_				,				pixel_values=A_				,				attention_mask=A_)

									self.assertEqual(output['''text_embeds'''].shape				,				(input_ids.shape[0], config.projection_dim))
									self.assertEqual(output['''image_embeds'''].shape				,				(pixel_values.shape[0], config.projection_dim))




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       int				,				A_     :       Tuple				,				A_     :       str=None				,				**A_     :       Any):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= self.get_vision_text_model(A_				,				A_)
									lowerCAmelCase_			:     Optional[int]									= {'''vision_model''': vision_model, '''text_model''': text_model}
									lowerCAmelCase_			:     Tuple									= FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_)

									lowerCAmelCase_			:     Tuple									= model(input_ids=A_				,				pixel_values=A_				,				attention_mask=A_)

									self.assertEqual(output['''text_embeds'''].shape				,				(input_ids.shape[0], model.config.projection_dim))
									self.assertEqual(output['''image_embeds'''].shape				,				(pixel_values.shape[0], model.config.projection_dim))




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[int]				,				A_     :       Tuple				,				A_     :       int				,				A_     :       Dict				,				A_     :       Union[str, Any]=None				,				**A_     :       Any):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= self.get_vision_text_model(A_				,				A_)
									lowerCAmelCase_			:     List[Any]									= {'''vision_model''': vision_model, '''text_model''': text_model}
									lowerCAmelCase_			:     List[Any]									= FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_)

									lowerCAmelCase_			:     Optional[Any]									= model(input_ids=A_				,				pixel_values=A_				,				attention_mask=A_)
									lowerCAmelCase_			:     int									= output[0]

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(A_)
											lowerCAmelCase_			:     str									= FlaxVisionTextDualEncoderModel.from_pretrained(A_)

											lowerCAmelCase_			:     Optional[Any]									= model(input_ids=A_				,				pixel_values=A_				,				attention_mask=A_)
											lowerCAmelCase_			:     Dict									= after_output[0]
											lowerCAmelCase_			:     List[Any]									= np.amax(np.abs(out_a - out_a))
											self.assertLessEqual(A_				,				1e-3)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       int				,				A_     :       Optional[int]				,				A_     :       List[str]				,				A_     :       Optional[Any]				,				A_     :       int=None				,				**A_     :       Any):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Union[str, Any]									= self.get_vision_text_model(A_				,				A_)
									lowerCAmelCase_			:     Dict									= {'''vision_model''': vision_model, '''text_model''': text_model}
									lowerCAmelCase_			:     Union[str, Any]									= FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_)

									lowerCAmelCase_			:     int									= model(
									    input_ids=A_				,				pixel_values=A_				,				attention_mask=A_				,				output_attentions=A_)

									lowerCAmelCase_			:     Optional[int]									= output.vision_model_output.attentions
									self.assertEqual(len(A_)				,				vision_config.num_hidden_layers)

									# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowerCAmelCase_			:     Tuple									= to_atuple(vision_model.config.image_size)
									lowerCAmelCase_			:     Any									= to_atuple(vision_model.config.patch_size)
									lowerCAmelCase_			:     str									= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									lowerCAmelCase_			:     Any									= num_patches + 1
									self.assertEqual(vision_attentions[0].shape[-3:]				,				(vision_config.num_attention_heads, seq_len, seq_len))

									lowerCAmelCase_			:     Optional[Any]									= output.text_model_output.attentions
									self.assertEqual(len(A_)				,				text_config.num_hidden_layers)

									self.assertEqual(
									    text_attentions[0].shape[-3:]				,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])				,				)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Any				,				A_     :       Tuple				,				A_     :       Tuple):
									pt_model.to(A_)
									pt_model.eval()

									# prepare inputs
									lowerCAmelCase_			:     int									= inputs_dict
									lowerCAmelCase_			:     Union[str, Any]									= {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

									with torch.no_grad():
											lowerCAmelCase_			:     Any									= pt_model(**A_).to_tuple()

									lowerCAmelCase_			:     Dict									= fx_model(**A_).to_tuple()
									self.assertEqual(len(A_)				,				len(A_)				,				'''Output lengths differ between Flax and PyTorch''')
									for fx_output, pt_output in zip(fx_outputs[:4]				,				pt_outputs[:4]):
											self.assert_almost_equals(A_				,				pt_output.numpy()				,				4e-2)

									# PT -> Flax
									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(A_)
											lowerCAmelCase_			:     List[Any]									= FlaxVisionTextDualEncoderModel.from_pretrained(A_				,				from_pt=A_)

									lowerCAmelCase_			:     Optional[Any]									= fx_model_loaded(**A_).to_tuple()
									self.assertEqual(len(A_)				,				len(A_)				,				'''Output lengths differ between Flax and PyTorch''')
									for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]				,				pt_outputs[:4]):
											self.assert_almost_equals(A_				,				pt_output.numpy()				,				4e-2)

									# Flax -> PT
									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(A_)
											lowerCAmelCase_			:     Union[str, Any]									= VisionTextDualEncoderModel.from_pretrained(A_				,				from_flax=A_)

									pt_model_loaded.to(A_)
									pt_model_loaded.eval()

									with torch.no_grad():
											lowerCAmelCase_			:     Dict									= pt_model_loaded(**A_).to_tuple()

									self.assertEqual(len(A_)				,				len(A_)				,				'''Output lengths differ between Flax and PyTorch''')
									for fx_output, pt_output_loaded in zip(fx_outputs[:4]				,				pt_outputs_loaded[:4]):
											self.assert_almost_equals(A_				,				pt_output_loaded.numpy()				,				4e-2)




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Tuple				,				A_     :       Tuple				,				A_     :       str):
									lowerCAmelCase_			:     Dict									= VisionTextDualEncoderConfig.from_vision_text_configs(A_				,				A_)

									lowerCAmelCase_			:     Any									= VisionTextDualEncoderModel(A_)
									lowerCAmelCase_			:     int									= FlaxVisionTextDualEncoderModel(A_)

									lowerCAmelCase_			:     Optional[Any]									= convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,				A_)
									lowerCAmelCase_			:     int									= fx_state

									self.check_pt_flax_equivalence(A_				,				A_				,				A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       List[str]				,				A_     :       Any				,				A_     :       Optional[Any]):
									lowerCAmelCase_			:     Optional[int]									= VisionTextDualEncoderConfig.from_vision_text_configs(A_				,				A_)

									lowerCAmelCase_			:     Union[str, Any]									= VisionTextDualEncoderModel(A_)
									lowerCAmelCase_			:     Dict									= FlaxVisionTextDualEncoderModel(A_)

									lowerCAmelCase_			:     Optional[int]									= load_flax_weights_in_pytorch_model(A_				,				fx_model.params)

									self.check_pt_flax_equivalence(A_				,				A_				,				A_)




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     int									= self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Optional[int]									= self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**A_)




							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= self.prepare_config_and_inputs()
									self.check_save_load(**A_)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     List[str]									= self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**A_)




							@is_pt_flax_cross_test
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     List[Any]									= self.prepare_config_and_inputs()
									lowerCAmelCase_			:     str									= config_inputs_dict.pop('''vision_config''')
									lowerCAmelCase_			:     Optional[Any]									= config_inputs_dict.pop('''text_config''')

									lowerCAmelCase_			:     Tuple									= config_inputs_dict

									self.check_equivalence_pt_to_flax(A_				,				A_				,				A_)
									self.check_equivalence_flax_to_pt(A_				,				A_				,				A_)







							@slow
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= self.get_pretrained_model_and_inputs()

									lowerCAmelCase_			:     Any									= model_a(**A_)
									lowerCAmelCase_			:     int									= outputs[0]

									with tempfile.TemporaryDirectory() as tmp_dirname:
											model_a.save_pretrained(A_)
											lowerCAmelCase_			:     int									= FlaxVisionTextDualEncoderModel.from_pretrained(A_)

											lowerCAmelCase_			:     int									= model_a(**A_)
											lowerCAmelCase_			:     Optional[int]									= after_outputs[0]
											lowerCAmelCase_			:     Optional[Any]									= np.amax(np.abs(out_a - out_a))
											self.assertLessEqual(A_				,				1e-5)





@require_flax
class 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     int									= FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-vit'''				,				'''hf-internal-testing/tiny-bert'''				,				vision_from_pt=A_				,				text_from_pt=A_				,				)
									lowerCAmelCase_			:     int									= 1_3
									lowerCAmelCase_			:     int									= floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ])
									lowerCAmelCase_			:     int									= ids_tensor([batch_size, 4]				,				model.config.text_config.vocab_size)
									lowerCAmelCase_			:     str									= random_attention_mask([batch_size, 4])
									lowerCAmelCase_			:     List[Any]									= {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Dict				,				A_     :       List[Any]):
									lowerCAmelCase_			:     str									= FlaxViTModel(A_)
									lowerCAmelCase_			:     int									= FlaxBertModel(A_)
									return vision_model, text_model







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     int									= FlaxViTModelTester(self)
									lowerCAmelCase_			:     Any									= FlaxBertModelTester(self)
									lowerCAmelCase_			:     Any									= vit_model_tester.prepare_config_and_inputs()
									lowerCAmelCase_			:     int									= bert_model_tester.prepare_config_and_inputs()

									lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= vision_config_and_inputs

									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= text_config_and_inputs

									# make sure that cross attention layers are added
									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": attention_mask,
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									}





@require_torch
class 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     List[Any]									= FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-clip'''				,				'''hf-internal-testing/tiny-bert'''				,				vision_from_pt=A_				,				text_from_pt=A_				,				)
									lowerCAmelCase_			:     List[str]									= 1_3
									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ])
									lowerCAmelCase_			:     str									= ids_tensor([batch_size, 4]				,				model.config.text_config.vocab_size)
									lowerCAmelCase_			:     int									= random_attention_mask([batch_size, 4])
									lowerCAmelCase_			:     Tuple									= {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       List[Any]				,				A_     :       str):
									lowerCAmelCase_			:     List[str]									= FlaxCLIPVisionModel(A_)
									lowerCAmelCase_			:     int									= FlaxBertModel(A_)
									return vision_model, text_model







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     List[str]									= FlaxCLIPVisionModelTester(self)
									lowerCAmelCase_			:     Dict									= FlaxBertModelTester(self)
									lowerCAmelCase_			:     Any									= clip_model_tester.prepare_config_and_inputs()
									lowerCAmelCase_			:     Any									= bert_model_tester.prepare_config_and_inputs()

									lowerCAmelCase_					,  lowerCAmelCase_			:     Union[str, Any]									= vision_config_and_inputs

									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     int									= text_config_and_inputs

									# make sure that cross attention layers are added
									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": attention_mask,
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									}





@require_flax
@require_vision
class 				__snake_case						(     unittest.TestCase						):




							@slow
							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     int									= FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''				,				logit_scale_init_value=1.0)
									lowerCAmelCase_			:     str									= VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

									lowerCAmelCase_			:     str									= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
									lowerCAmelCase_			:     Optional[int]									= processor(
									    text=['''una foto di un gatto''', '''una foto di un cane''']				,				images=A_				,				padding=A_				,				return_tensors='''np''')

									lowerCAmelCase_			:     Any									= model(**A_)

									# verify the logits
									self.assertEqual(outputs.logits_per_image.shape				,				(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
									self.assertEqual(
									    outputs.logits_per_text.shape				,				(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])				,				)

									lowerCAmelCase_			:     Any									= np.array([[1.228_4727, 0.310_4122]])

									self.assertTrue(np.allclose(outputs.logits_per_image				,				A_				,				atol=1e-3))












import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"  )
       torch.save(model.state_dict() ,     UpperCamelCase__  )


if __name__ == "__main__":
       a__:      str									=		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





