"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   AutoConfig.from_pretrained(lowerCAmelCase__     )
		lowerCAmelCase__        =   FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__     )
		lowerCAmelCase__        =   checkpoints.load_tax_checkpoint(lowerCAmelCase__     )

		lowerCAmelCase__        =   'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

		if config.model_type == "t5":
				lowerCAmelCase__        =   'SelfAttention'
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				lowerCAmelCase__        =   'LocalSelfAttention'
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				lowerCAmelCase__        =   'TransientGlobalSelfAttention'
		else:
				raise ValueError(
				    """Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`"""
				    """ attribute with a value from [\'local\', \'transient-global]."""     )

		# Encoder
		for layer_index in range(config.num_layers     ):
				lowerCAmelCase__        =   f"""layers_{str(lowerCAmelCase__     )}"""

				# Self-Attention
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

				# Layer Normalization
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

				if split_mlp_wi:
						lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
						lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
				else:
						lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				lowerCAmelCase__        =   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				lowerCAmelCase__        =   flax_model.params['encoder']['block'][str(lowerCAmelCase__     )]['layer']
				lowerCAmelCase__        =   tax_attention_key
				lowerCAmelCase__        =   tax_attention_out
				lowerCAmelCase__        =   tax_attention_query
				lowerCAmelCase__        =   tax_attention_value

				lowerCAmelCase__        =   tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCAmelCase__        =   tax_global_layer_norm

				if split_mlp_wi:
						lowerCAmelCase__        =   tax_mlp_wi_a
						lowerCAmelCase__        =   tax_mlp_wi_a
				else:
						lowerCAmelCase__        =   tax_mlp_wi

				lowerCAmelCase__        =   tax_mlp_wo
				lowerCAmelCase__        =   tax_mlp_layer_norm

				lowerCAmelCase__        =   flax_model_encoder_layer_block

		# Only for layer 0:
		lowerCAmelCase__        =   tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
		lowerCAmelCase__        =   tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				lowerCAmelCase__        =   tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
				lowerCAmelCase__        =   tax_encoder_global_rel_embedding

		# Assigning
		lowerCAmelCase__        =   tax_model['target']['encoder']['encoder_norm']['scale']
		lowerCAmelCase__        =   tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers     ):
				lowerCAmelCase__        =   f"""layers_{str(lowerCAmelCase__     )}"""

				# Self-Attention
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

				# Layer Normalization
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
				    'scale'
				]

				# Encoder-Decoder-Attention
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
				lowerCAmelCase__        =   tax_enc_dec_attention_module['key']['kernel']
				lowerCAmelCase__        =   tax_enc_dec_attention_module['out']['kernel']
				lowerCAmelCase__        =   tax_enc_dec_attention_module['query']['kernel']
				lowerCAmelCase__        =   tax_enc_dec_attention_module['value']['kernel']

				# Layer Normalization
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

				# MLP
				if split_mlp_wi:
						lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
						lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
				else:
						lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				lowerCAmelCase__        =   tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				lowerCAmelCase__        =   flax_model.params['decoder']['block'][str(lowerCAmelCase__     )]['layer']
				lowerCAmelCase__        =   tax_attention_key
				lowerCAmelCase__        =   tax_attention_out
				lowerCAmelCase__        =   tax_attention_query
				lowerCAmelCase__        =   tax_attention_value

				lowerCAmelCase__        =   tax_pre_attention_layer_norm

				lowerCAmelCase__        =   tax_enc_dec_attention_key
				lowerCAmelCase__        =   tax_enc_dec_attention_out
				lowerCAmelCase__        =   tax_enc_dec_attention_query
				lowerCAmelCase__        =   tax_enc_dec_attention_value

				lowerCAmelCase__        =   tax_cross_layer_norm

				if split_mlp_wi:
						lowerCAmelCase__        =   tax_mlp_wi_a
						lowerCAmelCase__        =   tax_mlp_wi_a
				else:
						lowerCAmelCase__        =   tax_mlp_wi

				lowerCAmelCase__        =   tax_mlp_wo

				lowerCAmelCase__        =   txa_mlp_layer_norm

				lowerCAmelCase__        =   flax_model_decoder_layer_block

		# Decoder Normalization
		lowerCAmelCase__        =   tax_model['target']['decoder']['decoder_norm']['scale']
		lowerCAmelCase__        =   txa_decoder_norm

		# Only for layer 0:
		lowerCAmelCase__        =   tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
		lowerCAmelCase__        =   tax_decoder_rel_embedding

		# Token Embeddings
		lowerCAmelCase__        =   tax_model['target']['token_embedder']['embedding']
		lowerCAmelCase__        =   txa_token_embeddings

		# LM Head (only in v1.1 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				lowerCAmelCase__        =   tax_model['target']['decoder']['logits_dense']['kernel']

		flax_model.save_pretrained(lowerCAmelCase__     )
		print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Any													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
			)
			parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
			parser.add_argument(
			    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
			)
			__lowerCAmelCase    :							Dict													=						parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
			from PIL import Image

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
					    "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
					    "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




