

import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.distilbert.modeling_flax_distilbert import (
     FlaxDistilBertForMaskedLM,
     FlaxDistilBertForMultipleChoice,
     FlaxDistilBertForQuestionAnswering,
     FlaxDistilBertForSequenceClassification,
     FlaxDistilBertForTokenClassification,
     FlaxDistilBertModel,
 )






class  __A					(				unittest.TestCase			):






    def __init__(						self      :     List[str]     ,					UpperCAmelCase_      :     Optional[int]     ,					UpperCAmelCase_      :     List[Any]=13     ,					UpperCAmelCase_      :     Tuple=7     ,					UpperCAmelCase_      :     str=True     ,					UpperCAmelCase_      :     Any=True     ,					UpperCAmelCase_      :     Dict=True     ,					UpperCAmelCase_      :     Union[str, Any]=True     ,					UpperCAmelCase_      :     List[str]=99     ,					UpperCAmelCase_      :     int=32     ,					UpperCAmelCase_      :     List[str]=5     ,					UpperCAmelCase_      :     Any=4     ,					UpperCAmelCase_      :     Optional[int]=37     ,					UpperCAmelCase_      :     Optional[int]="gelu"     ,					UpperCAmelCase_      :     str=0.1     ,					UpperCAmelCase_      :     Optional[Any]=0.1     ,					UpperCAmelCase_      :     List[Any]=512     ,					UpperCAmelCase_      :     Optional[int]=16     ,					UpperCAmelCase_      :     Any=2     ,					UpperCAmelCase_      :     Dict=0.02     ,					UpperCAmelCase_      :     Optional[Any]=4     ,					):
        lowerCAmelCase					:      Optional[Any]           =   parent
        lowerCAmelCase					:      int           =   batch_size
        lowerCAmelCase					:      int           =   seq_length
        lowerCAmelCase					:      List[str]           =   is_training
        lowerCAmelCase					:      str           =   use_attention_mask
        lowerCAmelCase					:      List[str]           =   use_token_type_ids
        lowerCAmelCase					:      Optional[Any]           =   use_labels
        lowerCAmelCase					:      Dict           =   vocab_size
        lowerCAmelCase					:      Union[str, Any]           =   hidden_size
        lowerCAmelCase					:      int           =   num_hidden_layers
        lowerCAmelCase					:      Optional[Any]           =   num_attention_heads
        lowerCAmelCase					:      Optional[Any]           =   intermediate_size
        lowerCAmelCase					:      int           =   hidden_act
        lowerCAmelCase					:      Dict           =   hidden_dropout_prob
        lowerCAmelCase					:      Optional[Any]           =   attention_probs_dropout_prob
        lowerCAmelCase					:      Dict           =   max_position_embeddings
        lowerCAmelCase					:      Tuple           =   type_vocab_size
        lowerCAmelCase					:      int           =   type_sequence_label_size
        lowerCAmelCase					:      Optional[Any]           =   initializer_range
        lowerCAmelCase					:      Union[str, Any]           =   num_choices






    def 						lowercase__			(						self      :     Union[str, Any]     ):
        lowerCAmelCase					:      List[Any]           =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

        lowerCAmelCase					:      Any           =   None
        if self.use_attention_mask:
            lowerCAmelCase					:      str           =   random_attention_mask([self.batch_size, self.seq_length]     )

        lowerCAmelCase					:      Optional[Any]           =   DistilBertConfig(
            vocab_size=self.vocab_size     ,					dim=self.hidden_size     ,					n_layers=self.num_hidden_layers     ,					n_heads=self.num_attention_heads     ,					hidden_dim=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					dropout=self.hidden_dropout_prob     ,					attention_dropout=self.attention_probs_dropout_prob     ,					max_position_embeddings=self.max_position_embeddings     ,					initializer_range=self.initializer_range     ,					tie_weights_=a_     ,					)

        return config, input_ids, attention_mask






    def 						lowercase__			(						self      :     str     ):
        lowerCAmelCase					:      Dict           =   self.prepare_config_and_inputs()
        lowerCAmelCase					:      Dict           =   config_and_inputs
        lowerCAmelCase					:      Any           =   {"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict






@require_flax
class  __A					(				lowerCAmelCase    ,    unittest.TestCase			):
    lowerCAmelCase_				:		Dict							=				(
        (
            FlaxDistilBertModel,
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def 						lowercase__			(						self      :     Dict     ):
        lowerCAmelCase					:      Optional[int]           =   FlaxDistilBertModelTester(self     )






    @slow
    def 						lowercase__			(						self      :     Any     ):
        for model_class_name in self.all_model_classes:
            lowerCAmelCase					:      int           =   model_class_name.from_pretrained('distilbert-base-uncased'     )
            lowerCAmelCase					:      Tuple           =   model(np.ones((1, 1)     )     )
            self.assertIsNotNone(a_     )






@require_flax
class  __A					(				unittest.TestCase			):






    @slow
    def 						lowercase__			(						self      :     Tuple     ):
        lowerCAmelCase					:      str           =   FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'     )
        lowerCAmelCase					:      int           =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]     )
        lowerCAmelCase					:      int           =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
        lowerCAmelCase					:      List[str]           =   model(a_     ,					attention_mask=a_     )[0]
        lowerCAmelCase					:      int           =   (1, 11, 768)
        self.assertEqual(output.shape     ,					a_     )
        lowerCAmelCase					:      List[str]           =   np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]     )

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,					a_     ,					atol=1E-4     )     )









'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCAmelCase   : Any             =				[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]




def 	_A  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		if new_k not in sd:
			raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''							)

		if "dense" in k or "proj" in new_k:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,dtype=sd[new_k].dtype							)
		assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
	# make sure embedding.padding_idx is respected
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
	assert extra == [], F'''no matches found for the following tf keys {extra}'''
	return torch_model




def 	_A  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

	# convert model
	lowercase						:		Dict   					=		get_tf_weights_as_numpy(A							)
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']
	if dataset == "large":
		lowercase						:		str   					=		task_specific_params
	lowercase						:		List[str]   					=		convert_pegasus(A							,A							)
	torch_model.save_pretrained(A							)
	lowercase						:		Union[str, Any]   					=		torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight"							)
	sd.pop("model.encoder.embed_positions.weight"							)
	torch.save(A							,Path(A							) / "pytorch_model.bin"							)


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
					parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


