





from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
			import tensorflow as tf

			from transformers import TFBlipTextModel
			from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=9_9       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		seq_length
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_input_mask
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		projection_dim
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		dropout
				lowerCamelCase__								=		attention_dropout
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		scope
				lowerCamelCase__								=		bos_token_id





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

				lowerCamelCase__								=		None
				if self.use_input_mask:
					lowerCamelCase__								=		random_attention_mask([self.batch_size, self.seq_length]							)

				if input_mask is not None:
					lowerCamelCase__								=		input_mask.numpy()
					lowerCamelCase__				,	lowerCamelCase__								=		input_mask.shape
					lowerCamelCase__								=		np.random.randint(1       ,  seq_length - 1       ,  size=(batch_size,)							)
					for batch_idx, start_index in enumerate(_a							):
						lowerCamelCase__								=		1
						lowerCamelCase__								=		0

				lowerCamelCase__								=		self.get_config()

				return config, input_ids, tf.convert_to_tensor(_a							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return BlipTextConfig(
				    vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  projection_dim=self.projection_dim       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  dropout=self.dropout       ,  attention_dropout=self.attention_dropout       ,  max_position_embeddings=self.max_position_embeddings       ,  initializer_range=self.initializer_range       ,  bos_token_id=self.bos_token_id       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		TFBlipTextModel(config=_a							)
				lowerCamelCase__								=		model(_a       ,  attention_mask=_a       ,  training=_a							)
				lowerCamelCase__								=		model(_a       ,  training=_a							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)
				self.parent.assertEqual(result.pooler_output.shape       ,  (self.batch_size, self.hidden_size)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.prepare_config_and_inputs()
				lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		config_and_inputs
				lowerCamelCase__								=		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict






@require_tf
class 					__A    (    UpperCAmelCase__			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(TFBlipTextModel,) if is_tf_available() else ()
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		BlipTextModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=_a       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skip(reason='''Blip does not use inputs_embeds'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__								=		TFBlipTextModel.from_pretrained(_a							)
					self.assertIsNotNone(_a							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase=True							):



				'''simple docstring'''







				super().test_pt_tf_model_equivalence(allow_missing_keys=_a							)









"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



