



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel






@require_tf
class 						a_		:
							UpperCAmelCase			:		List[str]        =				PegasusConfig
							UpperCAmelCase			:		Union[str, Any]        =				{}
							UpperCAmelCase			:		str        =				"""gelu"""


							def __init__(				self   :						List[str]      ,						a_   :						int      ,						a_   :						Dict=1_3      ,						a_   :						List[Any]=7      ,						a_   :						Union[str, Any]=True      ,						a_   :						int=False      ,						a_   :						int=9_9      ,						a_   :						Union[str, Any]=3_2      ,						a_   :						Any=2      ,						a_   :						Optional[int]=4      ,						a_   :						Optional[Any]=3_7      ,						a_   :						Dict=0.1      ,						a_   :						str=0.1      ,						a_   :						List[Any]=4_0      ,						a_   :						Optional[Any]=2      ,						a_   :						Dict=1      ,						a_   :						Dict=0      ,						)			->					Dict:
										snake_case:    List[str]      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    Optional[Any]      							=seq_length
										snake_case:    str      							=is_training
										snake_case:    Any      							=use_labels
										snake_case:    Optional[int]      							=vocab_size
										snake_case:    Dict      							=hidden_size
										snake_case:    List[Any]      							=num_hidden_layers
										snake_case:    Optional[int]      							=num_attention_heads
										snake_case:    int      							=intermediate_size

										snake_case:    int      							=hidden_dropout_prob
										snake_case:    Any      							=attention_probs_dropout_prob
										snake_case:    Optional[Any]      							=max_position_embeddings
										snake_case:    Tuple      							=eos_token_id
										snake_case:    Union[str, Any]      							=pad_token_id
										snake_case:    int      							=bos_token_id


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[Any]:
										snake_case:    Any      							=ids_tensor([self.batch_size, self.seq_length - 1]      ,						self.vocab_size			)
										snake_case:    int      							=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)      ,						1			)
										snake_case:    Optional[int]      							=tf.concat([input_ids, eos_tensor]      ,						axis=1			)

										snake_case:    Any      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size			)

										snake_case:    int      							=self.config_cls(
										    vocab_size=self.vocab_size      ,						d_model=self.hidden_size      ,						encoder_layers=self.num_hidden_layers      ,						decoder_layers=self.num_hidden_layers      ,						encoder_attention_heads=self.num_attention_heads      ,						decoder_attention_heads=self.num_attention_heads      ,						encoder_ffn_dim=self.intermediate_size      ,						decoder_ffn_dim=self.intermediate_size      ,						dropout=self.hidden_dropout_prob      ,						attention_dropout=self.attention_probs_dropout_prob      ,						max_position_embeddings=self.max_position_embeddings      ,						eos_token_ids=[2]      ,						bos_token_id=self.bos_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.pad_token_id      ,						**self.config_updates      ,						)
										snake_case:    Dict      							=prepare_pegasus_inputs_dict(a_      ,						a_      ,						a_			)
										return config, inputs_dict




							def    UpperCamelCase     (				self   :						int      ,						a_   :						Tuple      ,						a_   :						int			)			->					str:
										snake_case:    Tuple      							=TFPegasusModel(config=a_			).get_decoder()
										snake_case:    Union[str, Any]      							=inputs_dict['input_ids']

										snake_case:    Union[str, Any]      							=input_ids[:1, :]
										snake_case:    List[Any]      							=inputs_dict['attention_mask'][:1, :]
										snake_case:    Optional[Any]      							=inputs_dict['head_mask']
										snake_case:    Union[str, Any]      							=1

										# first forward pass
										snake_case:    str      							=model(a_      ,						attention_mask=a_      ,						head_mask=a_      ,						use_cache=a_			)

										snake_case							,  snake_case:    Optional[Any]      							=outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case:    Any      							=ids_tensor((self.batch_size, 3)      ,						config.vocab_size			)
										snake_case:    Optional[int]      							=tf.cast(ids_tensor((self.batch_size, 3)      ,						2			)      ,						tf.inta			)

										# append to next input_ids and
										snake_case:    Union[str, Any]      							=tf.concat([input_ids, next_tokens]      ,						axis=-1			)
										snake_case:    int      							=tf.concat([attention_mask, next_attn_mask]      ,						axis=-1			)

										snake_case:    List[Any]      							=model(a_      ,						attention_mask=a_			)[0]
										snake_case:    Union[str, Any]      							=model(a_      ,						attention_mask=a_      ,						past_key_values=a_			)[0]

										self.parent.assertEqual(next_tokens.shape[1]      ,						output_from_past.shape[1]			)

										# select random slice
										snake_case:    List[Any]      							=int(ids_tensor((1,)      ,						output_from_past.shape[-1]			)			)
										snake_case:    List[str]      							=output_from_no_past[:, -3:, random_slice_idx]
										snake_case:    Dict      							=output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(a_      ,						a_      ,						rtol=1E-3			)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							)				->					Dict:





			"""simple docstring"""







			if attention_mask is None:
						snake_case:    Any      							=tf.cast(tf.math.not_equal(__UpperCAmelCase				,							config.pad_token_id					)				,							tf.inta					)
			if decoder_attention_mask is None:
						snake_case:    int      							=tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape				,							dtype=tf.inta					),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,							config.pad_token_id					)				,							tf.inta					),
						    ]				,							axis=-1				,							)
			if head_mask is None:
						snake_case:    str      							=tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
			if decoder_head_mask is None:
						snake_case:    Optional[int]      							=tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
			if cross_attn_head_mask is None:
						snake_case:    Optional[Any]      							=tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}






@require_tf
class 						a_		(     snake_case    ,   snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Any        =				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							UpperCAmelCase			:		List[Any]        =				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							UpperCAmelCase			:		Dict        =				(
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCAmelCase			:		Optional[Any]        =				True
							UpperCAmelCase			:		Union[str, Any]        =				False
							UpperCAmelCase			:		Tuple        =				False


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[Any]:
										snake_case:    int      							=TFPegasusModelTester(self			)
										snake_case:    Union[str, Any]      							=ConfigTester(self      ,						config_class=a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Tuple:
										self.config_tester.run_common_tests()




							def    UpperCamelCase     (				self   :						Tuple			)			->					int:
										snake_case:    Any      							=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*a_			)






@require_sentencepiece
@require_tokenizers
@require_tf
class 						a_		(     unittest.TestCase    ):
							UpperCAmelCase			:		Union[str, Any]        =				[
							    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
							    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
							]
							UpperCAmelCase			:		List[Any]        =				[
							    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							UpperCAmelCase			:		Tuple        =				"""google/pegasus-xsum"""


							@cached_property
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Any:
										return AutoTokenizer.from_pretrained(self.model_name			)


							@cached_property
							def    UpperCamelCase     (				self   :						Dict			)			->					int:
										snake_case:    Tuple      							=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
										return model


							def    UpperCamelCase     (				self   :						int      ,						**a_   :						int			)			->					Optional[int]:
										snake_case:    Optional[int]      							=self.translate_src_text(**a_			)
										assert self.expected_text == generated_words


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						Any			)			->					Union[str, Any]:
										snake_case:    Optional[int]      							=self.tokenizer(self.src_text      ,						**a_      ,						padding=a_      ,						return_tensors='tf'			)
										snake_case:    List[str]      							=self.model.generate(
										    model_inputs.input_ids      ,						attention_mask=model_inputs.attention_mask      ,						num_beams=2      ,						use_cache=a_      ,						)
										snake_case:    int      							=self.tokenizer.batch_decode(generated_ids.numpy()      ,						skip_special_tokens=a_			)
										return generated_words




							@slow
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Union[str, Any]:
										self._assert_generated_batch_equal_expected()









'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




