


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Any  ,    __A :			Dict				)	->		Optional[Any]:

       """simple docstring"""

       return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Dict  ,    __A :			Tuple  ,    __A :			Tuple="attention"				)	->		str:

       """simple docstring"""

       a_					:		List[Any]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
       a_					:		int							=  k_tmp.reshape(k_tmp.shape[0]  ,    k_tmp.shape[1] * k_tmp.shape[2]				)
       a_					:		Optional[int]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
       a_					:		Dict							=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]  ,    o_tmp.shape[2]				)
       a_					:		Any							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
       a_					:		Any							=  q_tmp.reshape(q_tmp.shape[0]  ,    q_tmp.shape[1] * q_tmp.shape[2]				)
       a_					:		List[str]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
       a_					:		Optional[int]							=  v_tmp.reshape(v_tmp.shape[0]  ,    v_tmp.shape[1] * v_tmp.shape[2]				)
       return k, o, q, v







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Optional[int]  ,    __A :			str  ,    __A :			List[Any]=False				)	->		int:

       """simple docstring"""

       if split_mlp_wi:
              a_					:		List[str]							=  params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
              a_					:		Union[str, Any]							=  params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
              a_					:		Any							=  (wi_a, wi_a)
       else:
              a_					:		List[str]							=  params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

       a_					:		Union[str, Any]							=  params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
       return wi, wo







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			List[Any]  ,    __A :			str  ,    __A :			int				)	->		Tuple:

       """simple docstring"""

       return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]







def        SCREAMING_SNAKE_CASE_						(			__A :			dict  ,    *, __A :			int  ,    __A :			bool  ,    __A :			bool = False				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  traverse_util.flatten_dict(variables['target']				)
       a_					:		List[str]							=  {'/'.join(__A				): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       a_					:		Optional[int]							=  'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:'  ,    __A				)

       a_					:		List[str]							=  collections.OrderedDict()

       # Shared embeddings.
       a_					:		List[str]							=  old['token_embedder/embedding']

       # Encoder.
       for i in range(__A				):
              # Block i, layer 0 (Self Attention).
              a_					:		Tuple							=  tax_layer_norm_lookup(__A  ,    __A  ,    'encoder'  ,    'pre_attention_layer_norm'				)
              a_							,		a_							,		a_							,		a_					:		List[Any]							=  tax_attention_lookup(__A  ,    __A  ,    'encoder'  ,    'attention'				)
              a_					:		Optional[int]							=  layer_norm
              a_					:		Tuple							=  k.T
              a_					:		Tuple							=  o.T
              a_					:		Optional[Any]							=  q.T
              a_					:		List[str]							=  v.T

              # Block i, layer 1 (MLP).
              a_					:		List[Any]							=  tax_layer_norm_lookup(__A  ,    __A  ,    'encoder'  ,    'pre_mlp_layer_norm'				)
              a_							,		a_					:		Any							=  tax_mlp_lookup(__A  ,    __A  ,    'encoder'  ,    __A				)
              a_					:		Union[str, Any]							=  layer_norm
              if split_mlp_wi:
                     a_					:		Union[str, Any]							=  wi[0].T
                     a_					:		int							=  wi[1].T
              else:
                     a_					:		Tuple							=  wi.T
              a_					:		int							=  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     a_					:		Dict							=  tax_relpos_bias_lookup(
                         __A  ,    __A  ,    'encoder'				).T

       a_					:		Optional[Any]							=  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              a_					:		int							=  tax_relpos_bias_lookup(
                  __A  ,    0  ,    'encoder'				).T
              a_					:		str							=  tax_relpos_bias_lookup(
                  __A  ,    0  ,    'decoder'				).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__A				):
                     # Block i, layer 0 (Self Attention).
                     a_					:		str							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_self_attention_layer_norm'				)
                     a_							,		a_							,		a_							,		a_					:		Tuple							=  tax_attention_lookup(__A  ,    __A  ,    'decoder'  ,    'self_attention'				)
                     a_					:		int							=  layer_norm
                     a_					:		Optional[int]							=  k.T
                     a_					:		List[str]							=  o.T
                     a_					:		Any							=  q.T
                     a_					:		int							=  v.T

                     # Block i, layer 1 (Cross Attention).
                     a_					:		int							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_cross_attention_layer_norm'				)
                     a_							,		a_							,		a_							,		a_					:		str							=  tax_attention_lookup(__A  ,    __A  ,    'decoder'  ,    'encoder_decoder_attention'				)
                     a_					:		List[str]							=  layer_norm
                     a_					:		str							=  k.T
                     a_					:		Optional[Any]							=  o.T
                     a_					:		Tuple							=  q.T
                     a_					:		int							=  v.T

                     # Block i, layer 2 (MLP).
                     a_					:		Any							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_mlp_layer_norm'				)
                     a_							,		a_					:		int							=  tax_mlp_lookup(__A  ,    __A  ,    'decoder'  ,    __A				)
                     a_					:		int							=  layer_norm
                     if split_mlp_wi:
                            a_					:		List[Any]							=  wi[0].T
                            a_					:		List[str]							=  wi[1].T
                     else:
                            a_					:		int							=  wi.T
                     a_					:		List[str]							=  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            a_					:		List[str]							=  tax_relpos_bias_lookup(__A  ,    __A  ,    'decoder'				).T

              a_					:		Dict							=  old['decoder/decoder_norm/scale']

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     a_					:		Any							=  old['decoder/logits_dense/kernel'].T

       return new







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			bool				)	->		List[Any]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              a_					:		Dict							=  state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     a_					:		int							=  state_dict['shared.weight']

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'				)
                     a_					:		Any							=  state_dict['shared.weight']

       return state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			int  ,    __A :			int  ,    __A :			List[str]  ,    __A :			int				)	->		int:

       """simple docstring"""

       a_					:		str							=  checkpoints.load_tax_checkpoint(__A				)
       a_					:		Optional[Any]							=  convert_tax_to_pytorch(
           __A  ,    num_layers=config.num_layers  ,    is_encoder_only=__A  ,    scalable_attention=__A				)
       a_					:		Dict							=  make_state_dict(__A  ,    __A				)
       model.load_state_dict(__A  ,    strict=__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			str  ,    __A :			bool = False  ,    __A :			bool = False  ,    )	->		Optional[Any]:

       """simple docstring"""

       a_					:		Dict							=  MTaConfig.from_json_file(__A				)
       print(F"""Building PyTorch model from configuration: {config}"""				)
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              a_					:		Tuple							=  UMTaEncoderModel(__A				)
       else:
              a_					:		Union[str, Any]							=  UMTaForConditionalGeneration(__A				)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(__A  ,    __A  ,    __A  ,    __A  ,    __A				)

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
       model.save_pretrained(__A				)

       # Verify that we can load the checkpoint.
       model.from_pretrained(__A				)
       print('Done'				)


if __name__ == "__main__":
   UpperCAmelCase_      : Optional[int]  					=       argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
   # Required parameters
   parser.add_argument(
       '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
   )
   parser.add_argument(
       '--scalable_attention',
       action='store_true',
       help='Whether the model uses scaled attention (umt5 model)',
       default=False,
   )
   UpperCAmelCase_      : List[Any]  					=       parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )









import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class 						_A:


								"""simple docstring"""


								@staticmethod
								def 					UpperCAmelCase_		(			*_A     ,					**_A     ):
													pass







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					__A	:						str       =				hashlib.mda(image.tobytes()  )
					return m.hexdigest()[:10]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Dict       =				np.array(a  )
					__A	:						List[Any]       =				npimg.shape
					return {"hash": hashimage(a  ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
				UpperCamelCase    :  int					    =	dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				MaskGenerationPipeline(model=_A     ,					image_processor=_A     )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									pass



				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@slow
				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'     )

									__A	:						List[str]       =				image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[Any]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]     ,					)
									# fmt: on





				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				'facebook/sam-vit-huge'
									__A	:						List[str]       =				pipeline('mask-generation'     ,					model=_A     )

									__A	:						Tuple       =				image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[str]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]     ,					)




